My blogging life has been split in two! So mostly I’ll now be blogging over at Hack The Multiverse – the official D-Wave blog (formerly rose.blog) – about the fun I’ve been having programming quantum computers. You might want to check out the latest posts there as there will be a fair amount of activity going on! I’m going to reserve P&C for discussing other things (probably mostly to do with AI, AGI, etc.)

The new edition of my Gothic Fall book is now available!

For those who have no idea what I am talking about, let me explain:
One of my main hobbies is fantasy and dark art (which is mainly in the form of digital painting, although I do paint with acrylics too) and I have published a book of these works along with accompanying poetry. The first edition of the book was published in Spanish, but now the English version is available! Quote from the site:

“If you never leave home without wearing black you will enjoy this potent combination of black magic, graveyards, knee high boots, chains, candelabras, wings, cathedrals and masks. Suzanne Gildert is a UK based artist, specialising in fantasy, gothic and dark art. She combines traditional drawing and painting methods with more modern digital techniques to establish a unique blend of elements and an easily recognisable style.”

Here is the link to the book:
Buy GF book online!

I’m really happy that this edition has finally been released. it has given me a new motivation do do more artwork My next challenge is to get the book on the shelves in Chapters!

Enjoy!

Many fanciful ideas from science fiction stories of the past have indeed turned out to be part of our everyday lives today. And with a little further thought, computronium seems nowhere near as crazy as, say, flying cars, warp drives and lightsabres. In fact, most people would accept the idea of that transistors have been getting smaller and more efficient, marching to the beat of Moore’s law. We now take for granted that the processor of a cell phone would once have required an entire building and consume several kilowatts of power. So what is wrong with taking this idea to its ultimate limit? Why can’t transistors become smaller and smaller until they are the size of atoms, perhaps even the size of sub-atomic bits of matter? Wouldn’t that be something at least very similar to computronium, if not the real-deal itself?

Processing power

Let’s pick apart some of the statements above and think a little more carefully about them. To begin, consider the phrase ‘computational power’. This may seem easy to define. One could use FLOPs (floating point operations per second) as a definition. Or perhaps we could use the ‘clock speed’ of the processor – just how fast can we get those bits of matter to change state?

When looking for good metrics for the computational power of a processor, we come across some difficulties. You can’t just use the ‘clock speed’ of your computer. Imagine, for example, that you split the processor it into multiple cores. Some programs might now be able to run faster, even if the ‘clock speed’ was the same!

Imagine that you required a processor to perform transforms on lots of pixels at once – say you wanted a game engine where the state of each pixel was only related to the state of that pixel on the previous frame. (I.e. it didn’t care what all the other pixels were doing). A parallel processor would calculate the output of pixel values very effectively, as it could assign each pixel with its own core, and compute them all at the same time. In fact, the existence of this type of problem is why we often find that games consoles have Graphics-Processing-Units (GPUs) built in them. They are parallel processors which are good at doing pixel-transforms in this way.

But imagine instead if you wanted to perform a calculation which was more like the following scenario: Calculating the 100th value of the recursive equation z_n+1=z_n²+3z_n+1. There’s not really an easy way you can parallelize this, because each time you calculate the value of z in the equation, you need to know the result from the previous calculation. Everything has to be done in order. So if you tried to run this on your thousand core GPU, it would only really be able to make use of one of the cores, and then your highly parallel processor wouldn’t look so efficient after all.

In fact, the only reason why metrics such as clock speed and FLOPs have been useful up to now is that we have mostly been using our processors to do very similar tasks, such as arithmetic. But the kind of things we want them to do in the future, such as natural language processing and complex fluid dynamics simulations no longer rely on straightforward arithmetic operations.

We start to see the the computational power of a piece of matter really depends upon what you want it to do!

Arranging matter usefully

So although in the future we may be able to make atomic-size transistors, we still need to decide how to arrange those transistors. If we arrange them in a slightly different way, the programs that we run on them may suddenly become much more efficient. Constructing serial and parallel processors is just one way to think about rearranging your matter to compute differently. There are many other trade-offs, for example how the processor accesses memory, and whether it is analog or digital in nature.

Perhaps, then, to get around this problem, we could create a piece of matter that reprograms itself, that rearranges the atoms depending upon what you wanted to do. Then, you could have a processor with one large core if it is running a serial algorithm (like the recursive equation), and many small cores if it is running a parallel algorithm (like the game engine) Aha! That would get around this problem. Then my computronium can compute anything once more, in the most efficient way possible for a particular task.

However, we find that you still cannot win, even with this method. The ‘reprogramming of the matter’ stage would require a program all of its own to be run on the matter. The more clever your reprogramming program, the more time your processor would spend reconfiguring itself, and less time would be spent actually solving problems! You also have to somehow know in advance how to write the program that reprograms your matter, which again requires knowledge of what problems you might want to solve.

Computing limits

You may be wondering why I haven’t yet mentioned the idea of the Universal Turing Machine [2], which is a theoretical description of a computer, able to compute anything. Can we not just arrange our atoms to make a Turing Machine, that could then run any program? It is certainly the case that you can run any classical digital program on a Turing machine, but the theory says nothing about how efficient its computation would be. If you would like an analogy, a Turing Machine is to a real computer program as an abacus is to Excel – there is no reason why you cannot sit down and do your weekly accounts using an abacus, but it might take you a very long time!

We find when we try to build Turing machines in real life that not everything is realistically computable. A Turing Machine in practice and a Turing Machine in principle are two very different beasts. This is because we are always limited by the resources that our real-world Turing machine has access to (it is obvious in our analogy that there is a limit to how quickly we can move the beads on the abacus). The efficiency of a computer is ALWAYS related to how you assemble it in the real world, what you are trying to do with it, and what resources you have available. One should be careful when dealing with models that assume no resource constraints. Just think how different the world would be if we had an unlimited supply of free, clean energy.

The delicate matter of computation

Let’s philosophise about matter and computation some more. Imagine that the thing you wanted to do was to compute the energy levels of a Helium atom (after all, this is a Physics blog). You could use a regular desktop computer and start to do some simulations of Helium atoms using the quantum mechanical equations. Instead, you could take a few real helium atoms and measure the spectrum of light emitted from them when they are excited with a voltage inside an (otherwise evacuated) tube. The point is that a single Helium atom is able to compute the spacing of its own energy levels using far fewer resources than our simulation (which may require several tens of grams of silicon processor).

So as we see, atoms are already very busy computing things. In fact, you can think of the Universe as computing itself. So in a way, matter is already computronium, because it is very efficiently computing itself. But we don’t really want matter to do just that. We want it to compute the things that WE care about (like the probability of a particular stock going up in value in the next few days). But in order to make the Universe compute the things that we care about, we find that there is an overhead – a price to pay for making matter do something different from what it is meant to do. Or, to give a complementary way of thinking about this: The closer your computation is to what the original piece of matter would do naturally, the more efficient it will be.

So in conclusion, we find that we always have to cajole matter into computing the things we care about. We must invest extra resources and energy into the computation. We find that the best way to arranging computing elements depends upon what we want them to do. There is no magic ‘arrangement of matter’ which is all things to all people, no fabled ‘computronium’. We have to configure the matter in different ways to do different tasks, and the most efficient configuration varies vastly from task to task.

Afterthoughts: Some philosophical fun

The following amusing (if somewhat contrived) story serves to illustrate the points I have been making. Imagine I am sitting in my apartment, musing over how my laptop seems to generate a lot of heat from running this wordprocessing software. I dream of a much more efficient computronium-like processor on which I can perform my wordprocessing vastly more efficiently.

Suddenly, the heating in my apartment fails. Shivering with the cold, I am now in no mood to wordprocess, however I notice that my laptop processor has suddenly become very efficient at performing a task which is of much greater relevance to me (namely how to warm up my legs). That processor is now much closer to being a nice little piece of computronium as far as solving my immediate problem goes…

[1] – Computronium – http://en.wikipedia.org/wiki/Computronium

[2] – Universal Turing Machine – http://en.wikipedia.org/wiki/Universal_turing_machine

If you haven’t seen the talk, and would like to watch it, here it is:

However, this article is written as a standalone resources, so don’t worry if you haven’t seen the talk.

What I would like to do is start exploring some of those issues on this blog. So, here is my attempt to describe the first of the points that I set out to try and explore in the talk. I’ve used a slightly modified argument, to try and complement the talk for those who have already seen it.

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Pavlov’s AI:
What do superintelligences really want?

S. Gildert November 2010

(Photo © Thomas Saur)

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Humans are pretty intelligent. Most people would not argue with this. We spend a large majority of our lives trying to become MORE intelligent. Some of us spend nearly three decades of our lives in school, learning about the world. We also strive to work together in groups, as nations, and as a species, to better tackle the problems that face us.

Fairly recently in the history of man, we have developed tools, industrial machines, and lately computer systems to help us in our pursuit of this goal. Some particular humans (specifically some transhumanists) believe that their purpose in life is to try and become better than human. In practice this usually means striving to live longer, to become more intelligent, healthier, more aware and more connected with others. The use of technology plays a key role in this ideology.

A second track of transhumanism is to facilitate and support improvement of machines in parallel to improvements in human quality of life. Many people argue that we have also already built complex computer programs which show a glimmer of autonomous intelligence, and that in the future we will be able to create computer programs that are equal to, or have a much greater level of intelligence than humans. Such an intelligent system will be able to self-improve, just as we humans identify gaps in our knowledge and try to fill them by going to school and by learning all we can from others. Our computer programs will soon be able to read Wikipedia and Google Books to learn, just like their creators.

A perfect scientist?

But the design of our computer programs can be much more efficient in places where we, as humans, are rather limited. They will not get ‘bored’ in mathematics classes. They will work for hours on end, with no exhaustion, no fatigue, no wandering thoughts or daydreams. There would be no need for such a system to take a 2-hour lunch break, to sleep, or to worry about where its next meal will come from. The programs will also be able to analyze data in many more interesting ways than a human could, perhaps becoming a super-scientist. These programs will be far greater workers, far greater scholars, perhaps far greater citizens, than we could ever be.

It will be useful in analyzing the way such a machine would think about the world by starting with an analysis of humans. Why do humans want to learn things? I believe it is because there is a reward for doing so. If we excel in various subjects, we can get good jobs, a good income, and time to spend with others. By learning about the way the world works and becoming more intelligent, we can make our lives more comfortable. We know that if we put in the hard work, eventually it will pay off. There seem to be reward mechanisms built into humans, causing us to go out and do things in the world, knowing that there will be a payoff. These mechanisms act at such a deep level that we just follow them on a day-to-day basis – we don’t often think about why they might be there. Where do these reward mechanisms come from? Let’s take an example:

Why do you go to work every day?
To make money?
To pay for the education of your children?
To socialize and exchange information with your peers?
To gain respect and status in your organization?
To win prizes, to achieve success and fame?

I believe that ALL these rewards – and in fact EVERY reward – can be tied back to a basic human instinct. And that is the instinct to survive. We all want to survive and live happily in the world, and we also want to ensure that our children and those we care about have a good chance of surviving in the world too. In order to do this, and as our society becomes more and more complex, we have to become more and more intelligent to find ways to survive, such as those in the list above. When you trace back through the reasoning behind each of these things, when you strip back the complex social and personal layers, the driving motivations for everything we do are very simple. They form a small collection of desires. Furthermore, each one of those desires is something we do to maximize our chance at survival in the world.

So all these complex reward mechanisms we find in society are built up around simple desires. What are those desires? Those desires are to eat, to find water, to sleep, to be warm and comfortable, to avoid pain, to procreate and to protect those in our close social group. Our intelligence has evolved over thousands of years to make us better and better at fulfilling these desires. Why? Because if we weren’t good at doing that, we wouldn’t be here! And we have found more and more intelligent ways of wrapping these desires in complex reward mechanisms. Why do we obfuscate the underlying motivations? In a world where all the other members of the species are trying to do the same thing, we must find more intelligent, more complex ways of fulfilling these desires, so that we can outdo our rivals. Some of the ways in which we go about satisfying basic desires have become very complex and clever indeed! But I hope that you can see through that veil of complexity, to see that our intelligence is intrinsically linked to our survival, and this link is manifested in the world as these desires, these reward mechanisms, those things that drive us.

Building intelligent machines

Now, after that little deviation into human desires, I shall return to the main track of this article! Remember earlier I talked about building machines (computer systems) that may become much more intelligent than we are in the future. As I mentioned, the belief that this is possible is a commonly held view. In fact, most people not only believe that this is possible, but that such systems will self-improve, learn, and boost their own intelligence SO QUICKLY that once they surpass human level understanding they will become the dominant species on the planet, and may well wipe us out in the process. Such scenarios are often portrayed in the plotlines of movies, such as ‘Terminator’, or ‘The Matrix’.

I’m going to argue against this. I’m going to argue that the idea of building something that can ‘self-improve’ in an unlimited fashion is flawed. I believe there to be a hole in the argument. That flaw is uncovered when we try to apply the above analysis of desires and rewards in humans to machine intelligences. And I hope now that the title of this article starts to make sense – recall the famous experiments done by Pavlov [1] in which a dog was conditioned to expect rewards when certain things happened in the world. Hence, we will now try to assess what happens when you try to condition artificial intelligences (computer programs) in a similar way.

In artificial intelligence, just as with humans, we find that the idea of reward crops up all the time. There is a field of artificial intelligence called reinforcement learning [2], which is the idea of teaching a computer program new tricks by giving it a reward each time it gets something right. How can you give a computer program a reward? Well, just as an example, you could have within a computer program a piece of code (a mathematical function) which tries to maximize a number. Each time the computer does something which is ‘good’, the number gets increased.

The computer program therefore tries to increase the number, so you can make the computer do ‘good things’ by allowing it to ‘add 1′ to its number every time it performs a useful action. So a computer can discover which things are ‘good’ and which things are ‘bad’ simply by seeing if the value of the number is increasing. In a way the computer is being ‘rewarded’ for a good job. One would write the code such that the program was also able to remember which actions helped to increase its number, so that it can take those actions again in the future. (I challenge you to try to think of a way to write a computer program which can learn and take useful actions but doesn’t use a ‘reward’ technique similar to this one. It’s actually quite hard.)

Even in our deepest theories of machine intelligence, the idea of reward comes up. There is a theoretical model of intelligence called AIXI, developed by Marcus Hutter [3], which is basically a mathematical model which describes a very general, theoretical way in which an intelligent piece of code can work. This model is highly abstract, and allows, for example, all possible combinations of computer program code snippets to be considered in the construction of an intelligent system. Because of this, it hasn’t actually ever been implemented in a real computer. But, also because of this, the model is very general, and captures a description of the most intelligent program that could possibly exist. Note that in order to try and build something that even approximates this model is way beyond our computing capability at the moment, but we are talking now about computer systems that may in the future may be much more powerful. Anyway, the interesting thing about this model is that one of the parameters is a term describing… you guessed it… REWARD.

Changing your own code

We, as humans, are clever enough to look at this model, to understand it, and see that there is a reward term in there. And if we can see it, then any computer system that is based on this highly intelligent model will certainly be able to understand this model, and see the reward term too. But – and here’s the catch – the computer system that we build based on this model has the ability to change its own code! (In fact it had to in order to become more intelligent than us in the first place, once it realized we were such lousy programmers and took over programming itself!)

So imagine a simple example – our case from earlier – where a computer gets an additional ’1′ added to a numerical value for each good thing it does, and it tries to maximize the total by doing more good things. But if the computer program is clever enough, why can’t it just rewrite it’s own code and replace that piece of code that says ‘add 1′ with an ‘add 2′? Now the program gets twice the reward for every good thing that it does! And why stop at 2? Why not 3, or 4? Soon, the program will spend so much time thinking about adjusting its reward number that it will ignore the good task it was doing in the first place!
It seems that being intelligent enough to start modifying your own reward mechanisms is not necessarily a good thing!

But wait a minute, I said earlier that humans are intelligent. Don’t we have this same problem? Indeed, humans are intelligent. In fact, we are intelligent enough that in some ways we CAN analyze our own code. We can look at the way we are built, we can see all those things that I mentioned earlier – all those drives for food, warmth, sex. We too can see our own ‘reward function’. But the difference in humans is that we cannot change it. It is just too difficult! Our reward mechanisms are hard-coded by biology. They have evolved over millions of years to be locked into our genes, locked into the structure of the way our brains are wired. We can try to change them, perhaps by meditation or attending a motivational course. But in the end, biology always wins out. We always seem to have those basic needs.

All those things that I mentioned earlier that seem to limit humans – that seem to make us ‘inferior’ to that super-intelligent-scientist-machine we imagined – are there for a very good reason. They are what drive us to do everything we do. If we could change them, we’d be in exactly the same boat as the computer program. We’d be obsessed with changing our reward mechanisms to give us more reward rather than actually being driven to do things in the world in order to get that reward. And the ability to change our reward mechanisms is certainly NOT linked to survival! We quickly forget about all those things that are there for a reason, there to protect us and drive us to continue passing on our genes into the future.

So here’s the dilemna – we either hard code reward mechanisms into our computer programs – which means they can never be as intelligent as we are – they must never be able to see or adjust those reward mechanisms or change them. The second option is that we allow the programs full access to be able to adjust their own code, in which case they are in danger of becoming obsessed with changing their own reward function, and doing nothing else. This is why I refer to as humans being self-consistent – we see our own reward function but we do not have access to our own code. It is also the reason why I believe super-intelligent computer programs would not be self-consistent, because any system intelligent enough to understand itself AND change itself will no longer be driven to do useful things in the world and to continue improving itself.

In Conclusion:

In the case of humans, everything that we do that seems intelligent is part of a large, complex mechanism in which we are engaged to ensure our survival. This is so hardwired into us that we do not see it easily, and we certainly cannot change it very much. However, superintelligent computer programs are not limited in this way. They understand the way that they work, can change their own code, and are not limited by any particular reward mechanism. I argue that because of this fact, such entities are not self-consistent. In fact, if our superintelligent program has no hard-coded survival mechanism, it is more likely to switch itself off than to destroy the human race willfully.

Footnote:

As this analysis stands, it is a very simple argument, and of course there are many cases which are not covered here. But that does not mean they have been neglected! I hope to address some of these problems in subsequent posts, as including them here would make this article way too long.

[1] – Pavlov’s dog experiment – http://en.wikipedia.org/wiki/Classical_conditioning

[2] – Reinforcement Learning – http://en.wikipedia.org/wiki/Reinforcement_learning

[3] – AIXI Model, M Hutter el el. – http://www.hutter1.net/official/index.htm

Hmm, looking at this post in the browser is oddly fractal! Though not quite enough to become a Strange Loop. (H/T Douglas Hofstadter, you are awesome).

I would currently class myself as neither a proponent or a skeptic of the transhumanism movement. However I do love to explore and investigate the subject, as it seems to dance close to the very limits of our understanding of what is possible in the Universe. Can we learn something from analyzing the assumptions upon which this philosophical movement is based? I would answer not only yes, but that to do so yields one of the most exciting applications of the scientific method that we have encountered as a society.

I find myself increasingly drawn toward talking about how we can explore transhumanism from a more rational and objective point of view. I think all transhumanists should be obliged to take this standpoint, to avoid falling into a trap of dogmatic delusion. By playing devil’s advocate and challenging some of the basic tenets and assumptions, I doubt any harm can be done. At the least those tenets and assumptions will have to be rethought. But moreover, we may find that the lessons learned from encountering philosophical green lights and stop signs may inform the way we steer our engineering of the future.

I’ve thus decided to shift the focus of this blog a little towards some of these ideas. In a way I have already implemented some of this shift: I have written a couple of essays and posts before. But from now on, expect to see a lot more of this in the future. A blog format is an excellent way of disseminating information on this subject: It is dynamic, and can in principle reach a large audience. I also think that it fits in well with the Physics and Cake ethos – applying the principles of Physics to this area will form a large part of the investigations. And, of course, everything should always be discussed over coffee and a slice of cake! Another advantage is that this is something that everyone can think about and contribute to. You don’t need an expensive lab or a PhD in theoretical Physics to muse over these issues. In a lot of cases, curiosity, rationality, and the patience to follow an argument is all that is necessary.

Top Ten Tranhumanist Technologies

Ooh, the alliteration I notice that AGI is at number 1. Let’s hope that we can actually work towards a good definition of AGI and solve some foundational issues along the way to keep it up there! Interestingly I think a few of these technologies have interesting crossovers, such as virtual reality, mind uploading and cybernetics. The more we advance progress in technology, the more these disciplines will become indistinguishable.

Anyway, I think this is a nice article as it gives an introduction to many of the things that transhumanists talk about over coffee and cake (or water and fruit if you are a Paleo).

Other talks in this session were given by Randal Koene, Geordie Rose, Alex Peake, Paul Rosenbloom, Adrian Stoica, Moran Cerf and Ben Goertzel.

My talk was entitled ‘Pavlov’s AI: What do superintelligences really want?’ I discussed the foundations of AGI, and what I believe to be a problem (or at least an interesting philosophical gold-seam) in the idea of building self-improving artificial intelligences. I’ll be writing a lot more on this topic in the future, hopefully in the form of essays, blogposts and papers. I think it is very important to assess what we are trying to do in the area of AI, what the overall objectives are, and looking at what we can build from an objective point of view is helpful in framing our progress.

The conference was livestreamed, which was great. I think my talk had around 500 viewers. Add to that the 200 or so in the lecture hall; 700 is a pretty big audience! Some of talks had over 1300 remote viewers. Livestreaming really is a great way to reach a much bigger audience than is possible with real-life events alone.

I didn’t get to see much of the Caltech campus, but the courtyard at the Beckman Institute where the conference was held was beautiful. I enjoyed the fact that coffee and lunch was served outside in the courtyard. It was very pleasant! Sitting around outside in L.A. in December was surprisingly similar to a British summer!

I got to talk to some great people. I enjoy transhumanism-focused conferences as the people you meet tend to have many diverse interests and multidisciplinary backgrounds.

I was very inspired to continue exploring and documenting my journey into the interesting world of AGI. One of the things I really love doing is looking into the fundamental science behind Singularity-focused technologies. I try to be impartial to this and give both an optimistic account of the promise of future technologies whilst maintaining a skeptical curiosity about whether such technologies are fundamentally possible, and what roadmaps might lead to their successful implementation. So stay tuned for more Skepto-advocate Singularity fun!

But the good news is, I will be giving the talk this weekend instead!

Here is the abstract (slightly updated as the talk will be a little longer than originally planned)

Thinking about the hardware of thinking:
Can disruptive technologies help us achieve uploading?

S. Gildert,
Teleplace, 28th November 2010
10am PST (1pm EST, 6pm UK, 7pm continental EU).

We are surrounded by devices that rely on general purpose silicon processors, which are mostly very similar in terms of their design. But is this the only possibility? As we begin to run larger and more brain-like emulations, will our current methods of simulating neural networks be enough, even in principle? Why does the brain, with 100 billion neurons, consume less than 30W of power, whilst our attempts to simulate tens of thousands of neurons (for example in the blue brain project) consumes tens of KW? As we wish to run computations faster and more efficiently, we might we need to consider if the design of the hardware that we all take for granted is optimal. In this presentation I will discuss the recent return to a focus upon co-design – that is, designing specialized software algorithms running on specialized hardware, and how this approach may help us create much more powerful applications in the future. As an example, I will discuss some possible ways of running AI algorithms on novel forms of computer hardware, such as superconducting quantum computing processors. These behave entirely differently to our current silicon chips, and help to emphasize just how important disruptive technologies may be to our attempts to build intelligent machines.

Here is a link to the Teleplace announcement.

Hope to see you there!

I’ve also put this essay in the Resources section as a permanent link.

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The not-so spooky after all ‘observer-with-a-hammer’ effect

S. Gildert November 2010

I’m so sick of people using phrases like this:

“Looking at, nay, even thinking about a quantum computer will destroy its delicate computation. Even scientists do not understand this strange and counter-intuitive property of quantum mechanics”

or worse:

“The act of a conscious observer making a measurement on a quantum computer whilst it is performing a calculation causes the wavefunction to collapse. The spooky nature of these devices means that they just don’t work when we are looking at them!”

ARGGHHHHH!!!!!!!!

These kind of phrases spread like viral memes because they are easy to remember and they pique people’s curiosity. People like the idea of anthropomorphizing inanimate systems. It makes them seem unusual and special. This misunderstanding, the idea that a quantum system somehow ‘cares’ or is emotionally sensitive to what a human is doing, is actually what causes this meme to perpetuate.

So I’m going to put a new meme out there into the-internet-ether-blogosphere-tubes. Maybe someone will pick up on this analogy and it will become totally viral. It probably won’t, because it seems pretty dull in comparison to spooky ethereal all-seeing quantum systems, but if it flicks a light switch in the mind of but a single reader, if on contemplating my words someone’s conceptual picture of quantum mechanics as a mystical, ever elusive resource is reduced even by the tiniest amount, then my work here will be done.

Memetic surgery

Let’s start by cutting the yukky tumorous part from this meme and dissecting it on our operating table:

“Looking at a quantum system changes it.”

Now I don’t necessarily disagree with this statement, but I think you need to define what you mean by ‘looking’….

Usually when physicists ‘look’ at things, they are trying to measure something to extract information from it. To measure something, you need to interact with it in some way or other. In fact, everything in the world interacts with many other things around it (that’s why Physics is interesting!). Everything one could ever wish to measure is actually sitting in a little bath of other things that are constantly interacting with it. Usually, we can ignore this and concentrate on the one thing we care about. But sometimes this interacting-background property can cause unwanted problems.

Measuring small things

Brownian motion can give us a nice example of a nasty background interaction. Imagine that a scientist wanted to investigate the repulsion (or attraction) of some tiny magnetic particles in a solution that had just precipitated out of an awesomely cool chemical reaction. (I don’t know why you’d want to do this, but scientists have some weird ideas). So she starts to take measurements of the positions of the little magnetic particles over time, and finds that they are not obeying the laws of magnetism. How dare they! What could be wrong with the experiment? So our good scientist takes the solution in her beaker and you start to adjust various parameters to try and figure out what is going on. It turns out that when she cools the solution, the particles start to behave more in line with what is expected. She figures that the Brownian motion – all the other molecules jostling and wiggling around near the magnetic particles – are actually kicking the experiment around, ruining the results. But by lowering the temperature, it is possible to stop the environment in which the particles sit from disturbing them as much.

In this example, the scientist was able to measure the positions of the particles with something like a ruler or a laser or some other cool technique, and it was fairly easy, even though the environment had become irritatingly convolved with our experiment. Once she had got around how to stop the interaction with the environment, then our experiment worked well.

Quantum systems are small, and small things are delicate. But quantum systems are so small that the environment, the ‘background-interaction’ around them, is no longer something that they, or we, can ignore. It pushes them around. In order to have a chance at engineering quantum systems, researchers have to isolate them carefully from the environment (or at least the bits of the environment that kick them around). Scientists spend a lot of time trying to stop the environment from interacting with their qubits. For example, superconducting processors need to be operated at very cold temperatures, in extremely low magnetic field environments. But I won’t digress into the experimental details. The main idea is that no matter how you build your quantum computer, you will have to solve this problem in some way or other. And even after all this careful engineering, the darn things still interact with the environment to some degree.

It gets worse

But with quantum systems, there is an extra problem. The problem is not just the environment. To illustrate this problem, I’ll propose another little story of the striving scientists.

Imagine that our scientists have developed a technique to measure the diameter of bird eggs using a robotic arm. The arm has a hand that grasps the eggs, measures them, and then displays the diameter on a neat built-in display. (Alternatively, you can Bluetooth the results to your iPhone, so the scientists tell me). Anyway, this robotic arm is so ridiculously precise that it can measure the diameter of eggs more accurately than any pair or vernier calipers, any laser-interferometer array or any other cool way of measuring eggs that has ever existed. The National Standards laboratories are intrigued.

However, there is a slight problem. Every time the robot tries to measure an egg, it breaks the darn thing. There is no way to get around this. The scientific breakthrough relating to the accuracy of the new machine comes from the fact that the robot squeezes the egg slightly. Try and change the way that the measurement is performed, and you just can’t get good results anymore. It seems that we just cannot avoid breaking the eggs. The interaction of the robot with the egg is ruining our experiment.

Of course, a robot-egg measuring system like this sounds ridiculous, but this is exactly the problem that we have with quantum systems. The measuring apparatus is huge compared to the quantum system, and it interacts with it, just like the pesky environment does. It pushes and squeezes our quantum system. The result is that anything huge that we use to try to perform a delicate measurement will break it. And worse still, we can’t just try to ‘turn it off completely’ like we could with the environment surrounding the particles in the solution. By the very nature of what we are trying to do, we need the measurement apparatus to interact with the qubits, otherwise how can we measure them? What a pain. We end up measuring a kind of qubit-environment-combination mess, just like trying to measure the diameter of a broken egg whose contents are running all over our robotic measurement apparatus.

I can’t stress enough how comparatively big and clumsy quantum measurement apparatus is. Whilst scientists are trying to build better measurement techniques that don’t have such a bad effect on quantum systems, ultimately you just can’t get around this problem, because the large-scale things that we care about are just not compatible with the small-scale of the quantum world.

This doesn’t mean that quantum computers aren’t useful. It just means that the information we can extract from such systems is not neat, clean and unique to the thing we were trying to measure. We have to ‘reconstruct’ information from the inevitable conglomerate that we get out of a measurement. In some cases, this is enough to help us do useful computations.

Hammering the message home

Nowhere here does one need to invoke any spookiness, consciousness, roles of the observer, or animal cruelty involving cats and boxes. In fact, the so-called ‘observer’ effect could perhaps be more appropriately termed the ‘observer-with-a-hammer’ effect. We take for granted that we can measure large classical systems, like the 0 or 1 binary states of transistors, without affecting them too much. But measuring a quantum system is like trying the determine the voltage states of a single transistor by taking a hammer to the motherboard and counting the number of electrons that ended up sticking to the end of it. It kind of upsets the computation that you were in the middle of. It’s not the observer that’s the problem here, it’s the hammer.

So, the perhaps-not-so-viral phraseology for one to take away from my relentless ranting is thus:

“When you try and measure a delicate quantum system with clumsy apparatus, you actually end up with a messy combination of both!”

Alternatively, you could say ‘you can’t make a quantum measurement without breaking a few eggs’ – But if that terrible pun sticks then I will forever be embarrassed.

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Building more intelligent machines: Can ‘co-design’ help?

S. Gildert November 2010

There are many challenges that we face as we consider the future of computer architectures, and as the type of problem that people require such architectures to solve changes in scale and complexity. A recent article written for HPCwire [1] on ‘co-design’ highlights some of these issues, and demonstrates that the High Performance Computing community is very interested in new visions of breakthrough system architectures. Simply scaling up the number of cores of current technologies seems to be getting more difficult, more expensive, and more energy-hungry. One might imagine that in the face of such diminishing returns, there could be innovations in architectures that are vastly different from anything currently in existence. It seems clear that people are becoming more open to the idea that something revolutionary in this area may be required to make the leap to ‘exascale’ machines and beyond. The desire for larger and more powerful machines is driving people to try to create more ‘clever’ ways of solving problems (algorithmic and software development), rather than just increasing the speed and sheer number of transistors doing the processing. Co-design is one example of a buzzword that is sneakily spreading these memes which hint at ‘clever’ computing into the HPC community.

Generalization and specialization

I will explain the idea of co-design by using a colorful biological analogy. Imagine trying to design a general purpose animal: Our beast can fly, run, swim, dig tunnels and climb trees. It can survive in many different environments. However, anyone trying to design such an animal would soon discover that the large wings prevented it from digging tunnels effectively; that the thick fur coat to survive the extreme cold was not helpful in achieving a streamlined, fast swimmer. Any animal that was even slightly more specialized in one of these areas would quickly out-compete our general design. Indeed, for this very reason, natural selection causes specialization and therefore great diversity amongst the species that we see around us. Particular species are very good at surviving in particular environments.

How does this tie in with computer processing?

The problems that processors are designed to solve today are mostly all very similar. One can view this as being a bit like the ‘environmental landscape’ that our general purpose creatures live in. If the problems that they encounter around their environment on a day-to-day basis are of the same type, then there is no reason to diversify. Similarly, a large proportion of all computing resources today address some very similar problems, which can be solved quite well using general purpose architectures such as Intel Centrino chips. These tasks include the calculations that underlie familiar everyday tasks such as word-processing, and displaying web pages. But there do exist problems that have been previously thought to be very difficult for computers to solve, problems which seem out of reach of conventional computing. Examples of such problems are face-recognition, realistic speech synthesis, the discovery of patterns in large amounts of genetic data, and the extraction of ‘meaning’ from poetry or prose. These problems are like the trees and cliffs and oceans of our evolutionary landscape. The general purpose animals simply cannot exploit these features, they cannot solve these problems, so the problems are typically ignored or deemed ‘too hard’ for current computing platforms.

But there are companies and industries that do care about these problems. They require computing power to be harnessed for some very specific tasks. A few examples include extracting information from genetic data in the biotechnology companies, improving patient diagnosis and medical knowledge of expert systems in the healthcare sector, improving computer graphics for gaming experiences in entertainment businesses, and developing intelligent military tools for the defense industry. These fields all require the searching and sorting of data in parallel, and the manipulation of data on a much more abstract level for it to be efficient and worthwhile. This parallel operation and abstraction is something that general purpose processors are not very good at. They can attempt such a feat, but it takes the power of a supercomputer-size machine to tackle even very small instances of these specialized problems, using speed and brute force to overwhelm the difficulty. The result is very expensive, very inefficient, and does not scale well to larger problems of the same type.

It is this incorporation of variety and structure, the addition of trees, cliffs and oceans, into our computational problems causes our general-purpose processors to be very inefficient at these tasks. So why not allow the processors to specialize and diversify, just like natural selection explores the problem environment defined by our biological needs?

Following nature’s example

Co-design attempts to address this problem. It tries to design solutions around the structure of the problem type, resulting in an ability to solve that one problem very well indeed. In practice this is done by meticulous crafting of both software and hardware in synchrony. This allows software which complements the hardware and utilizes subtleties in the construction of the processor to help speed things up, rather than software which runs on a general architecture and incurs a much larger overhead. The result is a blindingly fast and efficient special purpose architecture and algorithm that is extremely good at tackling a particular problem. Though the resulting processor may not be very good at certain tasks we take for granted using general-purpose processors, solving specialized problems instead can be just as valuable, and perhaps will be even more valuable in the future.

A selection of processors which are starting to specialize are discussed in the HPCwire article. These include MDGRAPE-3, which calculates inter-atomic forces, and Anton, a system specifically designed to model the behaviour of molecules and proteins. More common names in the processor world are also beginning to explore possible specializations. Nvidia’s GPU based architectures are gaining in popularity, and FPGA and ASIC alternatives are now often considered for inclusion in HPC systems, such as some of Xilinx’s products. As better software and more special purpose algorithms are written to exploit these new architectures, they become cheaper and smaller than the brute-force general purpose alternatives. The size of the market for these products increases accordingly.

The quantum processors built by D-Wave Systems [2] are a perfect example of specialized animals, and give an insightful look into some of the ideas behind co-design. The D-Wave machines don’t look much like regular computers. They require complex refrigeration equipment and magnetic shielding. They use superconducting electronics rather than semiconducting transistors. They are, at first inspection, very unusual indeed. But they are carefully designed and built in a way that allows an intimate match between the hardware and the software algorithm that they run. As such they are very specialized, but this property allows them to tackle very well a particular class of problems known as discrete optimization problems,. This class of problems may appear highly mathematical, but looks can be deceiving. It turns out that once you start looking, examples of these problems are found in many interesting areas of industry and research. Most importantly, optimization forms the basis of many of the problems mentioned earlier, such as pattern recognition, machine learning, and meaning analysis. These are exactly the problems which are deemed ‘too hard’ for most computer processors, and yet could be of incredible market value. In short, there are many, many trees, cliffs and oceans in our problem landscape, and a wealth of opportunity for specialized processors to exploit this wonderful evolutionary niche!

Co-design is an important ideas in computing, and hopefully it will open people’s minds to the potential of new types of architecture that they may never have imagined before. I believe it will grow ever more important in the future, as we expect a larger and more complex variety of problems to be solved by our machines. The first time one sees footage of a tropical rainforest, one can but stare in awe at the wonders of never-before-seen species, each perfectly engineered to efficiently solve a particular biological problem. I hope that in the future, we will open our eyes to the possibility of an eco-sphere of computer architectures, populated by similarly diverse, beautiful and unusual creatures.

[1] http://www.hpcwire.com/features/Compilers-and-More-Hardware-Software-Codesign-106554093.html

[2] http://www.dwavesys.com/

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The conference is physically located in Italy, but I unfortunately cannot make in in person, as I will be attending another workshop. I will therefore be giving the talk remotely via the teleconferencing software Teleplace.

Anyway, here is some information about the talk, kindly posted by Giulio Prisco:

Thinking about the hardware of thinking:
Can disruptive technologies help us achieve uploading?

My talk was reported on Slashdot via a Next Big Future writeup, which in turn linked to Giulio’s Teleplace blog! This level of coverage for a talk has been very interesting, I’ve never had anything linked from /. before. They unfortunately got my NAME WRONG which was most irritating. Although I’m fairly impressed now that if you Google for ‘my name spelt incorrectly + quantum computing’, it does actually ask if you meant ‘my name spelt correctly + quantum computing’ which is a small but not insignificant victory Note: I’m not actually going to write out my name spelt incorrectly out here, as it would diminish the SNR!!

The talk also prompted this guest post written by Matt Swayne on the Quantum Bayesian Networks blog. Matt was present at the talk.

I’ve had a lot of people asking if I will post the slides online. Well here they are:

LINK TO SLIDES for QUANTUM COMPUTING: SEPARATING HOPE FROM HYPE
Teleplace seminar, S. Gildert, 04/09/10

Or rather, that’s a direct link to them. They are also available along with the VIDEOS of the talk and a bunch of other lectures and stuff are on the Resources page. Here are the links to the VIDEOS of the talk, and look, you have so many choices!!

Here are a couple of screenshots from the talk:

I’m going to entitle the talk:

‘Quantum Computing: Separating Hope from Hype’
Saturday 4th September, 10am PST

“The talk will explain why quantum computers are useful, and also dispel some of the myths about what they can and cannot do. It will address some of the practical ways in which we can build quantum computers and give realistic timescales for how far away commercially useful systems might be.”

Here’s Giulio’s advertisement for the talk:
GIULIO’S BLOGPOST about quantum computing seminar which is much more explanatory than the briefly thrown together blogpost you are being subjected to here.

Anyone wishing to watch the talk can obtain a Teleplace login by e-mailing Giulio Prisco (who can be contacted via the link above). Teleplace is a piece of software that is simple to download and quick to install on your computer and has an interface a bit like Second life. Now is a great time to get an account, as there will be many more interesting lectures and events hosted via this software as the community grows. Note the time – 10am PST Saturday morning (as in West Coast U.S. time zone, California, Vancouver, etc.)

The seminar is also listed as a Facebook Event if you would like to register interest that way!

The main idea of the conference was to hold a satellite workshop to the Singularity Summit, with the purpose of sparking discussion around the topics of Substrate Independent Minds. See the carboncopies website for more information on that! Ah, I love the format of blogging. I’m explaining what happened at a workshop without having introduced the idea of what the workshop was trying to achieve or what our new organisation actually *is*. Well, I promise that I’ll get round to explaining it soon, but until then it will have to be a shadowy unknown. The carboncopies website is also in the process of being filled with content, so I apologise if it is a little skeletal at the moment!

One interesting thing that we tried to do with the workshop was to combine a real life and a virtual space component. It was an interesting experience trying to bring together VR and IRL. In a way it was very fitting for a workshop based around the idea of substrate independent minds. Here we were somewhat along the way to substrate independent speakers! I am hoping that this will inspire more people to run workshops in this way, which will force the technology to improve.

I was very pleased too see so many people turning out. We had about 30 people in meatspace and about another 15 in virtual space on both days. Giulio Prisco has some nice write-up material about the workshops, including PICTURES and VIDEOS! Here are the links to his posts:

General overview
First day in detail
Second day in detail

For a first attempt, I don’t think that things went too badly! The technology wasn’t perfect, but we gave it a good try. The main problem was with the audio. Teleplace, the conferencing software we were using, works well when everyone in online with a headset and mic, there are no feedback problems. However, when you try and include an entire room as one attendee, it becomes a little more tricky.

This could be improved by either everyone in the room having headsets and mics, and then having a mixer which incorporated all the input into a single Teleplace attendee. The other way is that everyone in the room could also be logged into Teleplace with their own headsets and mics. *Make* that Singularity happen, ooh yeah! (/sarcasm)

Phys. Rev. B. 82, 024511 (2010), R. Harris et al.

The paper itself is quite long (15 pages) but it really gives a great description of how an 8-qubit ‘portion’ of the processor is designed, fabricated, fit to a physical (quantum mechanical) model, calibrated, and then used to solve problems.

If you don’t have access to the Phys Rev B journal, you can read a free preprint of the article here. And if you’ve never tried reading a journal paper before, why not give it a go! (This is an experimental paper, which means there are lots of pretty pictures to look at, even if the Physics gets hard to follow). For example, a microphotograph of the 8-qubit cell:

Simulating Chemistry using Quantum Computers

Genomics Has Bad News For The Singularity

The basic argument that the article puts forward is that the cost of sequencing the human genome has fallen following a super-exponential trend over the past 10 years. And yet, we do not have amazing breakthroughs in drug treatment and designer therapies. So how could we expect to have “genuine artificial intelligence, self-replicating nanorobots, and human-machine hybrids” even though Moore’s law is ensuring that the cost of processing power is falling? And it is falling at a much slower rate than genome sequencing costs!

The article states:

“In less than a decade, genomics has shown that improvements in the cost and speed of a technology do not guarantee real-world applications or immediate paradigm shifts in how we live our day-to-day lives.”

I feel however, that the article is somewhat comparing apples and oranges. I have two arguments against the comparison:

The first is that sequencing the genome just gives us data. There’s no algorithmic component. We still have little idea of how most of the code is actually implemented in the making of an organism. We don’t have the protein algorithmics. It’s like having the source code for an AGI without a compiler. But we do have reasonable physical and algorithmic models for neurons (and even entire brains!), we just lack the computational power to simulate billions of neurons in a highly connected structure. We can simulate larger and larger neural networks as hardware increases in speed, connectivity, and efficiency. And given that the algorithm is ‘captured’ in the very structure of the neural net, the algorithm advances as the hardware improves. This is not the case in genome sequencing.

The second argument is that sequencing genomes is not a process that can be bootstrapped. The very process of knowing a genome sequence isn’t going to help us sequence genomes faster or help you engineer designer drugs. But building smart AI systems – or “genuine artificial intelligence” as the article states – CAN enable you to bootstrap the process, as you will have access to copyable capital for almost no cost: Intelligent machines which can be put to the task of designing more intelligent machines. If we can build AIs that pass a particular threshold in terms of being able to design improved algorithmic versions of themselves, why should this be limited by hardware requirements at all? Moore’s law really just gives us an upper bound on the resources necessary to build intelligent systems if we approach the problem using a brute-force method.

We still need people working on the algorithmic side of things in AI – just as we need people working on how genes are actually expressed and give rise to characteristics in organisms. But in the case of AI, we already have an existence proof for such an object – the human brain, and so even with no algorithmic advances, we should be able to build one in-silico. Applications for genomics do not have such a clearly defined goal based on something that exists naturally (though harnessing effetcs like the way in which cancer cells avoid apoptosis might be a good place to start).

I’d be interested in hearing people thoughts on this.

I’m doing this for a couple of reasons. The first is that I like writing. The second is that a blog is not the best format to present ideas that you need to frequently refer to, and often when you get asked the same question by multiple people it is better to point them to a permanent resource rather than repeating yourself each time. The third is that I would like to add some ideas to the general thought-provoking mash-up of AGI memes around the internet. The fourth is that I think people enjoy reading short articles and opinion pieces somewhat more than entire books. The fifth is that (somewhat in contradiction to my previous reason) I’m hoping to eventually write a neat book about related topics, and whilst I have already started doing this, I feel that I need a lot more practice writing several-page documents before I feel I can make something that is 100+ pages long which I am happy with. Note, the PhD thesis does not count!!

So here we go. Click on the title to download the PDF.
You can also find a copy of the essay in the resources tab.
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ESSAY TITLE:
Language, learning, and social interaction
Insights and pitfalls on the road to AGI

Abstract:
Why is language important? What purpose does it serve on a fundamental level? How is it related to the ways in which we learn? Why did it evolve? In this short essay I’m going to take a Darwinian look at language and why we must be careful when considering the role played by language in the building of intelligent machines which are not human-like.

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Not only is this a Physics-based educational and entertainment extravaganza, but the video is interspersed with some cool shots of my old lab at Birmingham, and my old dilution refrigerator – I miss you, Frosty… *sniff*

It even includes some of my trademark cute pen and paper diagrams like this one:

How could you resist. Go on, have a read…..

Observation of Co-tunneling in Pairs of Coupled Flux Qubits

(I believe there is something called a ‘paper dance’ that I am supposed to be doing)….

Anyway, here I’ll try and write a little review article describing what this paper is all about. I’m assuming some knowledge of elementary quantum mechanics here. You can read up about the background QM needed here., and here.

First of all, what is macroscopic resonant tunneling (MRT)?

I’ll start by introducing energy wells. These are very common in the analysis of quantum mechanical systems. When you solve the Schrodinger equation, you put into the equation an energy landscape (also known as a ‘potential’), and out pop the wavefunctions and their associated eigenvalues (the energies that the system is allowed to have). This is usually illustrated with a square well potential, or a harmonic oscillator (parabolic) potential, like this:

Well, the flux qubit (quantum bit), which is what we build, has an energy landscape that looks a bit like a double well. This is useful for quantum computation as you can call one of the wells ’0′ and the other ’1′. When you measure the system, you find that the state will be in one well or the other, and the value of your ‘bit’ will be 0 or 1. The double well potential as you might imagine also contains energy levels, and the neat thing is that these energy levels can see each other through the barrier, because the wavefunction ‘leaks’ a little bit from one well into the neighbouring one:

One can imagine tilting the two wells with respect to one another, so the system becomes asymmetric and the energy levels in each well move with respect to one another. In flux qubit-land, we ’tilt’ the wells by applying small magnetic fields to the superconducting loops which form the qubits. Very crudely, when energy levels ‘line up’ the two wells see each other, and you can get quantum tunneling between the two states.

This effect is known as macroscopic resonant tunneling. So how do you measure it? You start by initializing the system so that state is localised in just one well (for example, by biasing the potential very hard in one direction so that there is effectively only one well), like this:

and then tilt the well-system back a little bit. At each tilt value, you stochastically monitor which well the state ends up in, then return it to the initialisation state and repeat lots and lots of times for different levels of tilt. As mentioned before, when the energy levels line up, you can get some tunneling and you are more likely to find the system on the other side of the barrier:

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In this way you can build up a picture of when the system is tunneling and when it isn’t as a function of tilt. Classically, the particle would remain mostly in the state it started in, until the tilt gets so large that the particle can be thermally activated OVER the barrier. So classically the probability of the state being found on the right hand side ‘state 1′ as a function of tilt looks something like this:

Quantum mechanically, as the energy levels ‘line up’, the particle can tunnel through the barrier – and so you get a little resonance in the probability of finding it on the other side (hence the name MRT). There are lots of energy levels in the wells, so as you tilt the system more and more, you encounter many such resonances. So the probability as a function of tilt now looks something like this:

This is a really cool result as it demonstrates that your system is quantum mechanical. There’s just no way you can get these resonances classically, as there’s no way that particle can get through the barrier classically.

Note: This is slightly different from macroscopic quantum tunneling, when the state tunnels out of the well-system altogether, in the same way that an alpha particle ‘tunnels’ out of the nucleus during radioactive decay and flies off into the ether. But that is a topic for another post.

So what’s all this co-tunneling stuff?

It’s all very nice showing that a single qubit is behaving quantum mechanically. Big deal, that’s easy. But stacking them together like qubit lego and showing that the resulting structure is quantum mechanical is harder.

Anyway, that is what this paper is all about. Two flux qubits are locked together by magnetic coupling, and therefore the double well potential is now actually 4-dimensional. If you don’t like thinking in 4D, you can imagine two separate double-wells, which are locked together so that they mimic each other. Getting the double well potentials similar enough to be able to lock them together in the first place is also really hard with superconducting flux qubits. It’s actually easier with atoms or ions than superconducting loops, because nature gives you identical systems to start with. But flux qubits are more versatile for other reasons, so the effort that has to go into making them identical is worthwhile.

Once they are locked together, you can again start tilting the ‘two-qubit-potential’. The spacing of the energy levels will now be different (think about a mass on the end of the spring – if you glue another mass to it, the resonant frequencies of the system will change, and the energies levels of the system along with them. We have sort of made our qubit ‘heavier’ by adding another one to it.

But we still see the resonant peaks! Which means that two qubits locked together still behave as a nice quantum mechanical object. The peaks don’t look quite as obvious as the ones I have drawn in my cartoon above. If you want to see what they really look like check out Figure 3 of the preprint. (Note that the figure shows MRT ‘rate’ rather than ‘probability’, but the two are very closely linked)

From the little resonant peaks that you see, you can extract Delta – which is a measure of the energy level spacing in the wells. In this particular flux-qubit system, the energy level spacing (and therefore Delta) can be tuned finely by using another superconducting loop attached to the main qubit loop. So you can make your qubit mass-on-a-spring effectively heavier or lighter by this method too. When the second tuning loop is adjusted, the resulting change in the energy level separation agrees well with theoretical predictions.

As you add more and more qubits, it gets harder to measure Delta, as the energy levels get very close together, and the peaks start to become washed out by noise. You can use the ‘tuning’ loop to make Delta bigger, but it can only help so much, as the tuning also has a side effect: It lowers the overall ‘signal’ level of the resonant peaks that you measure.

In summary:

Looking at the quantum properties of coupled qubits is very important, as it helps us experimentally characterise quantum computing systems.
Coupling qubits together makes them ‘heavier’ and their quantum energy levels become harder to measure.
Here two coupled qubits are still behaving quantum mechanically, so this is promising. This means that in the quantum computation occurring on these chips involves at least 2-qubits interacting in a quantum mechanical way. Physicists calls these ’2-qubit processes’. There may be processes of much higher order happening too.
This is pretty impressive considering that these qubits are surrounded by lots of other qubits, and connected to many, many other elements in the circuitry. (Most other quantum computing devices explored so far are much more isolated from other nearby elements).

The talks are all being livestreamed, which is pretty cool too. I can’t really describe the conference in words, so here are some pictures from the conference so far:

Audience pic:

General overview:

Here is a picture of Geordie’s talk about D-Wave, quantum computing and Intelligence:

Here is a picture of me next to the Aiken-IBM Automatic Sequence Controlled Calculator Mark I. This thing is truly amazing, a real piece of computing history.

The MIT museum was also really cool More news soon!

Let’s think for a second: Do we really have the right computational substrates for realising concepts such as highly connected neural networks, and thinking machines? Is the Von Neumann architecture really the best way to support such things? We are currently solving the problem of simulating intelligent systems by throwing more and more computational power at them. Though there’s something very odd about this, we have little choice, as we have become very good at designing processors that behave in a particular way, that talk to memory in a particular way, and that have a small number of cores. The problem with the commonly used architectures is that they just cannot embed things like neural nets, and other highly parallel structures, very efficiently.

Could adiabatic quantum processors be used for neural net and highly parallel processing purposes? Of course! The architectures are very compatible. AQO processors are very similar to Boltzmann machines, which underlie the operation of way many pattern-recognising systems, such as our own brain. There are other delicious fruits of superconducting logic, for example the ease with which we can implement reversible logic, and the exceedingly low power dissipation of such circuits. These systems also exhibit macroscopic quantum effects, which may be a great resource in computation, or it may not. But even if it does not, we should not ignore the fact that actually building such machines is the only way to answer this, and many other questions.

I think that superconducting processors are offering us a wonderful gift here, and yet we refuse to take advantage of it. Why? The reasons are a little hard to stomach.

It seems that while we’d rather just get going building some cool things, we end up spending a large proportion of our time and effort debating issues like whether or not quantum computers can solve NP-Complete problems in polynomial time. What?! I hear you say. My answer: Exactly. Obscure as they are, these questions are the most important thing in the world to a vanishingly small fraction of the population. Yet these seemingly lofty theoretical debates are casting a shadow over the use of superconducting QC architectures in every area of research, including things like the novel implementations of hardware-based neural nets, which may prove to be an even more fruitful avenue than optimization.

It will take a large amount of financing to commercialize general purpose superconducting processors, and an unimaginably large effort on behalf of the scientific researchers and engineers who devote their time to trying to progress this technology. The step from fundamental research to commercialisation cannot and will not work in an academic environment. Why? Because in order to fabricate integrated circuits of any kind you need investments of hundreds of millions of dollars into foundries. Robust and scalable technologies can only be realised in an industrial setting. Sure, small scale systems can be realised in many labs, but there are no industrial uses for devices produced in this setting: Anything that they demonstrate can be outperformed with a standard computing system. And it will stay that way until we realise that we need to take a risk as a society and really invest now in a technology of the future. I’ve seen distressing things happening at RSFQ roadmapping meetings. The conclusion of the roadmapping somewhat tragically boils down to ‘make another roadmap’ – because there are no applications beyond a few incredibly specialised circuits that can be realised within the scope of government research grants (~$10K-5M. Oh and by the way they aren’t very reproducible either). There is no funding on the necessary scale, and therefore whatever money is put into this distributed and broken field rather swiftly evaporates, even though if considered cumulatively it may have been enough to get a single, well focused effort going.

So, my plea to the academic world is to realise that there comes a point where quarrelling over things should be secondary to our solidarity as a community who really want to see something achieved. I’d rather try and fail than sit smugly in my ivory tower proclaiming that something is 20, or 50 years away (read – therefore not my problem guys!) Day after day people (with little or no knowledge in the field) exclaim that these things will never work, you are not building anything useful, your results are fraudulent, your devices are vapour-ware….etc etc.

This is more than cautious scientific scepticism, this is sheer hatred. It seems very unusual, and from my point of view, very detrimental to the entire field. I’d first advise such people to read some of our peer-reviewed papers to get their facts straight. I’d then say the following:

If those working in the fields of superconducting electronics, flux qubits, and quantum information really cared about the future of a technology that they had helped show had huge theoretical promise, they should rally along with us rather than spitefully burning their own bridges. As a community, we should promote the potential of our research and try to convince whoever needs convincing that these endeavours, these ‘Manhattan’ style projects are the only way in which we can bring disruptive technologies to fruition. And even if such projects fail, even if there is no other possible spin off that comes out of them, think about this: Such projects cost about the same amount as a couple of ICBMs. I know which I’d rather have more of in this world.

Here are some points:

svenhoek likes guns: “A ceiling? Wow, I think we have heard this kind of speak all through history. People that say this always are made to look stupid by history. plus, there will still be scientists needed to analyze the data and figures the machines bring up.”

But why shouldn’t there be a ceiling, just because there hasn’t been one up until now? To let someone else do the work for me, I think that this comment summarises nicely how I feel about this ‘ceiling’ argument:

Derek Pegritz: “I think one of the reasons there hasn’t been a major breakthrough in theoretical physics since the early days of quantum mechanics is simply that the baseline human mind is no longer capable of crunching the multidimensional data required to view the universe in its totality. As we are now, humans are perfectly capable of observing, analyzing, and describing the way in which the 4-dimensional submanifold our minds exist in primarily. Once you start adding dimensions and reduce physics to dealing with incredibly fine, emphemeral particle and field transactions, the 1.0 human mind is simply cannot envision the higher orders of logic which may obtain. This is not to say that we can’t see and understand certain *glimpses* of higher-order multidimensional physics–string theory, M theory, et. al. indicate that we can certainly comprehend it to *some* degree–but to really get to the heart of post-quantum physics, we’ll probably need a measure of intelligence amplification. Combining a machine-mind’s speed and data-crunching capacity with the human brain’s excellent pattern-matching faculties could very well lead to a true unified TOE.”

(There were several other comments along this line)
I do agree that humans mostly work by analogy and building up a mental picture of the world to whatever level of detail is necessary for us to survive. So the argument goes that we just aren’t evolved to be able to make logical arguments in N-dimensions – it doesn’t come naturally to us, so we use tools to help, but we are still at a disadvantage in that the links are no longer ‘obvious’ to us. How often have you heard the phrase ‘quantum mechanics is weird’ or ‘spooky’ – even Physicists don’t like QM because it is non-intuitive (it is also incomplete, but let’s not go there – it’s pretty good, and a better theory that closed some of those loopholes probably wouldn’t look *much* more ‘intuitive’ to us.)

Makidian: “I feel like only part of this will ever be true. I don’t think physicists will ever want to give up the aspect of their job that causes them to ask fundamental questions of everything. It would counterproductive and against what they have been working for to just have a computer do it for them. These futurists are so in love witht the idea of the singularity that they just start posing theories without considering that humans may want to do it and still work out the answer for themselves.
I’m all about making a better human to a certain degree, but not at the cost of losing my own humanity because isn’t that really the point!?”

I think the point here is not that they wouldn’t still want to do their job, but that that they would HAVE to give it up if something could do it much better than them, as no-one would pay them (at a higher rate) to continue. Physicists don’t sit around pondering the great problems of the Universe for fun – most do it to earn a living. At the least they would have to relegate it to a hobby (in the same way that people still build radios from discrete components). If machines do something better than people, they will be replaced, unless something about our global socio-economic system changes radically in the next few decades (which I won’t rule out completely). This is also assuming that human-physicists remain the same throughout this entire process, which I also do not believe will happen. We are already starting to augment ourselves as a community (how many of us read arXiv on smart-phones on the train in the morning?) There are many ways in which we can continue this trend, hence my point in the original post about experimentalists possible undergoing a human-machine merger just before their jobs get stolen

artum: “Enough of this Singularity BS. It’s just another get-immortal-quick scheme for morons. Also research of any kind like this would be a horrible idea because there’d be no one to confirm it as with ordinary science, only other science-bots”

This comment made me smile. Gotta love that bio-Luddite ad-hominem attack touch. Anyway, I think that the machines would very much enjoy sharing the information with each other, if their hardwired goal was to discover a model with which to predict the behaviour of the Universe, it would make them very happy! Much happier than it would humans, who tend to think: ‘Damn, I’ve been scooped again by my competitor’.

RandomThought: “As for the rest of your article, I personally have come to the opinion, that in general more stays the same than changes. People have always thought they were heading towards the big everything-changing event and somehow life mostly just goes on. “

The Singularity meme is a double edged sword. It gives people an idea of what I am talking about without me having to describe it in minute detail, but then again I have to accept all the baggage that comes along with it. So yes, I admit, I was being lazy, and I wasn’t necessarily referring to the Singularity in all its ‘wonderful glory’ but just rather the part where AI technologies start to become better at doing Physics than we are. Personally, this really does radically affect my life, seeing as I am a professional Physicist for a living

Just in case you missed the links:
Original post
Discussion over at io9

I think that there really won’t be much point in studying Physics post-singularity, as expert systems and AIs will be able to advance scientific development and understanding, put forward theories and perform experiments themselves. Or rather, there won’t be much point in studying it as humans. It would be like studying weaving, except there won’t be a market for quaint, slightly wobbly ‘hand-made theories’ and ‘hand-analysed data’ in the same way that people look for beads on strings at craft-fayres in country barns.

I’m afraid to say that the first guys out will be the theorists. Theorem proving machines are already gaining traction, and I don’t think it will be long before these systems will start to become commonplace, as soon as Artificial General Intelligence (AGI) gets even a foothold in this area. These kind of systems which are given the chance to play about in a theoretical, platonic realm of logic and inference constitute a nice step between AI and AGI, and they are likely to be exploited as such. Our beautiful theoretical physics will be demoted to a mere rung on the ladder of systems trying to become more ‘human’ and less like theoretical physicists!

Coming a close second will be computational Physicists. I mean, they need expert systems already to even start! Their only hope is to cling on to the ability to test their simulations against the real world – which an automated system might find tricky.

I think that experimentalists will be the last to go, as they interact the most closely with the material world… These brave soldiers will hold out the longest in the trenches of this war, fighting the new breed of scientist with their ability to gracefully control and manipulate the most delicate of scientific instruments. Instruments which are indeed the lower sentience predecessors of Intelligent Scientific Equipment. In fact, experimentalists might even hang around long enough to see the merging of humans with machines, in which case they can probably keep their jobs throughout

I think that the process of writing papers could probably be automated sooner than we think. But if our scientific equipment really does start to out-investigate us, who will read the papers? The equipment will just quietly and democratically communicate information across a global network, with each piece of kit having an instantly updated database of the current ‘cutting edge’ results. The system will know everything. It will have the capability to search and analyse every single paper ever written on a scientific subject. It will have a deduction and inference engine that can allow it to leapfrog any literature-savvy scientist in an instant, even with limited AGI capabilities. Such machines would not need to go to conferences, all necessary information can communicated almost effortlessly over the network. Peer review will probably still happen (systems will compare results), but it will be done via distributed computing, and it will be democratic – there’s no need for one system to be ‘better’ than another, these machines don’t care about getting first author on that Nature paper. They care about sharing the information and producing a model of the Universe that best describes it. This can be hardwired into their algorithms. It will be their very raison d’etre. They can become perfect scientists within their experimental capabilities (which will improve over time too).

2050, Researchers carrying piles of paperwork from their offices. (Many never did go paperless. Most of the yellowing documents are still from the 1990s)

2050-research-group: You can’t get a grant these days unless you’ve got the latest Auto-Prof-300. Nothing can analyse data quite like it! It doesn’t take tea breaks or lunch. It doesn’t claim conference expenses. It uses less electricity than our computers did, not to mention the savings on pens and envelopes! It even writes our papers for us, heh! There’s just no use for us anymore. But it’s OK, we have a plan. We’re going to start a museum

What is the point of all this rambling…. Well, I just thought I’d explain one of the reasons why I’m interested in AI and AGI. I think that we can develop AGI as a tool for improving the way we do Physics. As for the consequences of that, well I am not in a position to judge. Technology is agnostic, it will provide advantages and disadvantages for the human race as we know it. But being a Physicist, one is always looking for better ways to be a physicist, and to do better Physics. I feel that the best way to do Physics is to build machines to do Physics for us. After all, we’re good at building machines to do things for us. I also believe that there are fundamental reasons why we are not best placed as agents in this environment to do Physics anymore. I feel that we are approaching somewhat of a ‘ceiling’ regarding our own ability to understand the way in which the universe operates.

Hopefully this lighthearted and somewhat tongue-in-cheek post will be the forerunner to some more posts about how machines such as Auto-Prof-300 can actually be realised. I’ll also talk a bit more about why I believe this ‘ceiling’ to our understanding exists.

The Summit’s main theme is: Rise of the citizen scientist, and I do indeed look forward to having lots of interesting discussions about the future of science, in addition to (hopefully) meeting people with the specific intent of sparking AGI discussions and potentially ideas for projects. So let me know if you are going along and want to discuss Quantum Computing, AI/AGI, the future of science or anything else! The conference has a fantastic, diverse lineup of speakers, and the topics range from Controlling Brain Circuits with Light to Building a Services Market for the Transhuman Era. Pretty cool, huh?

I think it is really important that Physicists consider how science might be conducted in the upcoming decades. That is a subject for another blogpost, maybe even an essay or two – but I certainly think that the way we are currently going about making discoveries is rather inefficient, and there are several ways in which we can improve on this.

BANG! The Universe Verse (Book I). The book is a portrayal of how the laws of Physics as we know them today arose in the short period of time after the Big Bang. The book also explains how matter forms, and how nuclear fusion and stellar activity plays a significant role in explaining why the Universe appears as it does at present.

But the cool thing about the book is that is is presented in a comic book format, with two cute characters guiding you through the science. Here is an excerpt:

“The proton in the centre may not be alone
As another has access to this VIP Zone
The neutron may not be quite as attractive
But it is quiet, well mannered, and rarely reactive”

This would be great to read to kids

You can read the PDF version online or support the author and buy the book.

So, as I embark upon a new learning task, I start to wonder if there is anything I can ‘learn’ (hehe) from my previous experience. To be clear about my goal: I would like to formally learn more about the field of Artificial Intelligence.

So I thought I’d write down a few things that might help and hinder me in this task, and to try and extract just how comparable the two learning experiences might be.

.

1.) I know a lot more about learning now. I am fairly good at knowing where to get information, how to mine resources and when to give up and go onto something simpler first if you’re just not getting anywhere. So this will all help.

2.) I probably have about the same amount of time to do the learning for AI as I had for QC, in both cases the subjects are tangential to what I am/was doing at the time. Not central, but related.

3.) I am generally more focused than I was 7 years ago! I also pretty much don’t drink alcohol anymore, and I’m probably in better health overall, which will help towards the goal. So this is all positive.

4.) However I *am* older – perhaps I am losing some of my brain plasticity!

5.) I think the core conceptual framework of QC is more tightly knit, whereas with AI it is a bit more sprawling, however to counter this I’d say that the mechanics/mathematics of QC is probably more involved. I may be wrong here of course!

6.) It is easier to ‘start’ with AI, I think there are simple programs, simulations and experiments that you can work on DIY style to learn a lot (I know that I learn quite well that way)

7.) There is a crossover between QC and AI – which makes the learning more interesting, as you can apply one area of expertise to another and see if you can come up with anything interesting along the way.

8.) I am now much better at networking, talking to people and conferencing – and you learn a LOT through these techniques. It is so important to learn what has already been done so you don’t end up spending several years re-inventing the proverbial wheel…

9.) Technologies are accelerating. I can utilise the latest developments to help me learn a new topic. 7 years ago I wasn’t using Wikipedia, ArXiv, Google Books etc, etc…. and I can now start to organise my thoughts more intuitively using wikis and mindmaps. I have an almost constant connection to the internet at all times now – so not only do I have a much greater access to learning resources, but I also feel in a much more information-aware mindset.

10.) Black Swans – of course something crazy might happen, like, say (lets be optimistic here) we suddenly find we’ve gone through the Singularity… then I won’t have to worry quite so much about the learning anymore…

What will happen? Will it work? 7 years?

Let’s see…..

The talk is entitled ‘Playing with adiabatic hardware: From designer potentials to quantum brains’ although it certainly isn’t quite as ‘brain’ focused as some of the previous talks I have given, heh

.

Here are the other parts (they should be linked from that one, but just in case people can’t find them):

AQC Part 2
AQC Part 3
AQC Part 4
AQC Part 5
AQC Part 6

P.S. I wasn’t trying to be mean to the gate model (or computer scientists for that matter) – it just kinda happened…

P.P.S Some of the notation is a bit off – the J’s should be K’s to be consistent with the literature I believe…

To help me along the way, I recently purchased an Android-sporting Nexus One. The phone has been really great so far, I’m very pleased with it. Having the ability to read all my RSS feeds and keep up with the latest quant-ph ArXiv posts on my commute into work is just heavenly. I feel so connected. How did I ever live without this technology?? Google Maps with built-in GPS has been amazingly useful too. And I’ve only downloaded a handful of apps so far. If anyone knows of any good Android apps to a.) help me with Quantum Physics b.) allow me to explore the world of augmented reality, please let me know!

I’m looking forward to finding new ways to integrate with technology over the next couple of years. Perhaps some of them I will be able to help to bring to fruition directly. I’m just feeling now that I am getting back up to speed with everything, and I want to push further into the realm of really using technology to augment everything I do.

Apparently I’m one cool gadget behind the times…. though I’m certainly thinking about buying an iPad

Simulation of Classical Thermal States on a Quantum Computer: A Renormalization Group Approach
May edit this post when I do

And also:
Improved Superconducting Qubit Readout by Qubit-Induced Nonlinearities
Quantum money from knots
Adiabatic quantum computation: Enthusiast and Sceptic’s perspectives

Preparation and Measurement of Three-Qubit Entanglement in a Superconducting Circuit

Generation of Three-Qubit Entangled States using Superconducting Phase Qubits

Are Yale and SB going head-to-head in a gate-model phase/flux qubit battle? Looks like it…But who will win? Who has the highest T2?! Place your bets now!

More interestingly, this means we have yet another datapoint on the graph of number of successfully entangled sc gate model qubits versus time (in years). Presumably with 3 data points we can roughly gauge the scaling. I feel a plot may be required…and probably a log-lin scale.

Single atoms go transparent – PhysicsWorld April 21st 2010

The article was written in response to an ArXiv’ed paper from Nakamura’s group who investigate superconducting qubits (usually of the charge variety) in this case coupled to a microwave transmission line:

Electromagnetically induced transparency on a single artificial atom

From the article:

“Making an opaque material transparent might seem like magic. But for well over a decade, physicists have been able to do just that in atomic gases using the phenomenon of electromagnetically induced transparency (EIT). Now, however, this seemingly magical effect has been observed in single atoms – and in “artificial” atoms consisting of a superconducting loop – for the first time. “

I made the point in the article that combining flying qubits (basically entangled photons) which have the advantanges of long decoherence times and easy transportation of information, with solid-state implementations such as superconducting loops (good at storage and high-fidelity readout) would be a great step forwards in the generation of scalable architectures for quantum computing. Of course, I am slightly more biased towards the adiabatic QC approach at the moment (go figure) but there’s no reason why these developments couldn’t lead to some awesome hybrid quantum circuits involving a combination of gate model solid-state, AQC solid state and flying qubits. We just need to figure out how to put all the pieces together.

Part 2
Part 3
Part 4
Part 5
Part 6
Part 7

In my very young days, I would keep a diary because I was told to. Later, in high single-digit and early teen years I would keep a diary because it was somewhere I could write private thoughts, fulfilling the role of what some might have thought of as an ‘imaginary friend’ – I very much talked to my journal in a ‘dear diary’ style, as though it understood my concerns about the world.

My recent reasons for logging have generally been because I’m very busy, I’m enjoying life, and I’m doing a lot of things. I find it great to read back over my journal entries and relive the experiences. I especially like comparing the anticipation of an event with the memories of how it went and what I learnt from it. It really reinforces the idea of events which you may be nervous about never being as bad as you expect. It can be a really insightful thing to do.

Last weekend I spent a lot of time scanning old photographs into digital format. It’s amazing how each photograph opens up an entire set of memories, thoughts, and feelings. I’m also scanning my entire back archive of paper artwork (hundreds of a4 and a5 images). I like the idea of having all this stuff in a digital format such that it may eventually be tagged and have a proper semantic referencing system, when an appropriate framework for this kind of thing is developed.

However, I have a slightly more practical (and somewhat more controversial) reason for lifelogging, which I would like to explore in the next few years (or maybe decades).

Creating an upload from an extended lifelog

I like the idea of creating an AI that could take all this data and infer things from it. It could perhaps infer what kind of a person I was, and what kind of a person I am now. It might be a useful dataset for an AGI trying to understand human development, or developing itself.

An even more interesting idea is to create a virtual version of yourself by giving it access to all this information and a timeline. (You’d effectively be giving it memories).

One currently in vogue lifelogging technique is recording your entire set of experiences using an on-person video camera with built-in audio. However I feel that this method has its flaws. The stream obviously only records external input. You would ideally have a technique which also monitors streams such as internal reasoning, understanding, feelings and personal thoughts. Some of this could be automatically recorded using secondary effects, for example heart rate, hormone levels, blood sugar levels. But even those techniques just can’t capture that oh-so-elusive personal subjective experience.

Journal keeping is one way to do get around this problem, but you have to learn to write your journal in a very specific way. So something like “I listened to some music today” would be pretty information lean, whereas “I listened to song X today and it made me feel rather melancholy because it reminded me of the time when I first heard it, I was doing Y, and that inspired me to draw this piece of artwork Z. Now everytime I hear that piece of music I’m inspired to create more artwork”. In addition, I think that tagging stuff will be easier in text and image formats than in a video stream.

A dream diary can also contribute to the dataset, as it could give an AI more data about how the subjective experience during sleep can be different to normal.

In short, there’s no way to create an exhaustive dataset, but a sparse one may still be useful. I guess I’ll continue doing it as long as I find it fun.

So yesterday we had the red ink debacle which spurred me to write the Paper Algorithm:

1.) Write paper
2.) Give to colleague
3.) Get returned
4.) Shake off excess red ink.
5.) Rework.

Repeat steps 3-5 and hope for convergence.
Note this algorithm may have bad runtime scaling due to T(step 4) -T(step 3).

A friend of mine tried to suggest some further steps involving the journal submission process, but unfortunately those kind of delightful games are beyond my event horizon at the moment!

Here is a picture of the red ink debacle (which by the way looks worse now as I’m covered it in my own rebuttals of and agreements with the arguments – in black ink I hasten to add).

Anyway, the new version of the document is better for the corrections now but I fear it may have to be streamlined as it’s packing 7 pages of Physics awesomeness already… and I’m wondering about going further into the details of thermal escape from the washboard potential. Maybe I shouldn’t do that.

…which is a bad thing, as I don’t think I baked enough cakes for everyone in the Uni. Anyway, here are the ones I baked for the weekly gathering…

I’m going to be leaving the delightful world of academia to go and work for the even more delightful world of D-Wave Systems very soon. I’ll be working as an Experimental Physicist.

I’m very excited about this new job

I feel that academia has been an interesting and enjoyable experience in many respects, but in order to work on really big picture, far reaching projects one needs to move outside the University research-grant-oriented framework. I think I’ve mentioned on this blog before about what I call the ‘funding gap’ – there is a lack of grant support to push research from a fundamental level to a commercial level in many areas of science. University schemes exist to help spin-off companies to start up, but there is not always a smooth transition between research and market. In particular, there are cases where there is an inescapable need to really ENGINEER a technology to a commercial level, perhaps for several years.

I would class superconducting electronics and quantum computing as two such cases.

The project page is here

And here’s an IBM write-up of the effort

Also see here for a nice schematic of the device

“Unlike current processors, the CMOSAIC project considers a 3D stack-architecture of multiple cores with a interconnect density from 100 to 10,000 connections per millimeter square. Researchers believe that these tiny connections and the use of hair-thin, liquid cooling microchannels measuring only 50 microns in diameter between the active chips are the missing links to achieving high-performance computing with future 3D chip stacks.”

Just my personal opinion of course… but…. this seems like a case of fixing the symptoms rather than finding a cure. Will bringing a microfluidic angle into Moore’s law really help us out?

Why do we put up with this kind of heating problem in the first place? One could, for example, consider an alternative investment in the development of reversible, low disspation superconducting electronics.

I guess the project will be interesting just from a point of view of 3D manufacturing and incorporation of fluidics into microchips – this kind of technology could be indispensable for progress in areas such as lab-on-a-chip technology. But as far as raw processing power goes, this approach seems a bit like ignoring the elephant in the room.

Similar processes are used to make quantum computing chips, albeit with different materials. These semiconductor industry techniques have been developed over decades into the complex processes you see in this video. So understanding why it is so hard to make reproducible quantum processors should be obvious – we need to develop similarly complicated and well controlled processes for the new technologies.

H/T Kostas Hatalis

The DJs sport names such as ‘Diesel’, ‘Vermin’ and ‘Yumbolt’. Now if I were spinnin’ choonz at this club I would have called myself DJ Supersolid, but then again maybe they haven’t been keeping up to date with the latest developments in condensed matter physics.

The club has an ‘elements’ theme in general, with an Oxygen room, a Nitrogen room and a Carbon room. Cute. No Niobium room though, booo…

The project is designed to simulate what D-Wave’s adiabatic quantum optimization chips are doing during their quantum annealing cycles. The chips consist of coupled qubits which behave like an array of interconnected spins, in an Ising-type configuration. The spin system can be highly frustrated depending upon the sign and magnitude of the coupling between the spins and on the bias of each spin site. All possible configurations of the spins have an associated energy, and the chips try to find the configuration of the spins which minimises this energy.

The AQUA project simulates these spin systems by using a Quantum Monte Carlo technique to calculate the energy gap between the ground state of the system (the lowest energy ‘solution’ to the problem) and the first excited state (next best solution) for some ‘ultra hard’ problems (which you can think of as ones where the degree of frustration can be very high). You can look at the results for 8, 16, 32, 48, 72 and 96 qubit problems here:

AQUA@Home results so far

I love looking at these minimum gap curves, their statistical properties are very interesting. For example, you often get two minima regions in the energy gap as the system is annealed. There is also some very fine structure in the curves. I wonder if any generalizations can be made by analysing the similarities and differences of many such curves as the number of qubits increases.

You can help contribute to this project by downloading BOINC and donating spare CPU cycles (the program runs as a background task). The information for how to do this can be found under the Join AQUA@Home section on this page. Go, crowdsourced quantum science, go!!

So what have I learnt from this conference? (I’ll probably go into all these ideas in much more detail in subsequent posts, but for now I’m just putting down some thoughts.)

* AGI is a young field, with many disputes and disagreements, which makes the conference both interesting and useful.

* People seem very passionate about the subject, which manifests both as optimism about the field and fierce debates over the problems anticipated, and already being encountered.

* There is a wide range of people here with very diverse backgrounds. I’ve spoken to computer scientists, physicists, mathematicians, philoshophers, neuroscientists, software programmers, entrepreneurs, and many others.

* There is an interesting split between the theoretical (understanding, defining and bounding what AGI is) and the experimental (building candidate systems). It actually strikes me as being similar to the QIP community, except QIP has had about 20 extra years for the theory to race ahead of the experimental verification. I worry that the same might happen to AGI.

* There is another split, which is a bit more subtle, between those that believe that bio/brain inspired investigation can help push AGI forward, and those that believe it won’t – or even worse, that it might cause the field to go backward, by ‘distracting’ researchers who would be working on other potential areas.

* The major problem is that people still can’t agree on a definition of intelligence, or even if there is, or can be one.

* There is also a problem in that the people actually trying to build systems do not know what cognitive architectures will support full AGI, so lots of people are trying lots of different architectures, basically ‘stamp collecting’, until more rigorous theories of cognitive architecture emerge. Some (most) of the current architectures that are being used are bio-inspired.

* There were a few presentations that I thought were much closer to narrow AI than AGI, especially on the more practical side. I guess this is to be expected, but I didn’t get the feeling that the generalization of these techniques was being pursued with vigour.

Marcus Hutter
Hutter described the AIXI model, which is a theoretically best case version of a goal-driven intelligent system.

Mosche Looks
Looks discussed program learning and ways in which this can be implemented. Program learning involves a system ‘discovering’ a program via altering its own code.

Ben Goertzel
Goertzel talked about developments of AI agents interacting with virtual environments versus robots interacing with real world environments and discussed the advantages and disadvantages of both appraoches.

Randal Koene
Talked about whether or not we can find correlations between some of the concepts encountered in AI/AGI an some of the neuronal mechanisms that are occurring in the brain. The specific concept under scrutiny was reinforcement learning. Koene demonstrated that collections of neurons can be modelled by cortical minicolumns and by connecting thes together in large groups, they simulate the same behaviour as is seen in trials undertaken by monkeys given a reward / no reward mechanism for recongnising certain images.

Another thing I love is the lecture theatre/conference venue (The USI). There are power sockets at every seat and there is free, easy-to-connect wifi. I don’t think I’ve been to a single conference where both those criteria have been fulfilled before. Lunch is provided on-site, and coffee and snack breaks are scheduled often. Those are really the only things I ask in terms of the conference venue to be honest. Power, wifi, coffee, lunch.

Yesterday I had a great meal at one of the local Lugano pizzaria restaurants, and talked about AGI and what it actually means.

Anyway, more soon, it’s buffet-networking time now…

I had obviously been asleep before the ring. In fact, I remembered what I had been dreaming about. Specifically, I dreamt that I was in a completely different location. I was at the home of some of my close relatives, nowhere near where I live, and we were discussing something over a box of chocolates (not that I dream about chocolate all the time or anything) but the important point is that this was a totally different environment filled with a set of people undergoing complex social interactions. As an aside, one should note that I always dream as though everything is happening for real – I can never control dreams or be aware that they are actually only dreams, so this felt like a totally normal series of events, as would happen during an average day.

Anyway, because the telephone only rang once I was able to gain a temporal reference of the time frame over which the following occurred: My brain was able to change its mode of operation from self-referential to external input driven, my mental model of the world was substantially overhauled by awakening into a completely different environment, realising who I am, where I was, the fact that there was a telephone ringing in the next room, understanding what a telephone is, that it was some time in the early morning, that the telephone should really *not* be ringing at this time, and that I should probably be doing something about the fact that it is.

In a period of approximately 2 seconds, by the time the ring was over, I was contemplating whether or not to get up and try to answer it. I usually don’t have such an accurate temporal reference to the experience of awakening, as I awake either naturally or via my alarm clock which I have actually learned to turn off whilst still mostly asleep so I have no idea how long it takes me to ‘come round’ under those circumstances.

Interestingly, I recall another occasion whereby I was awoken by an earthquake. This was a very different experience, I was completely confused, presumably because my brain couldn’t latch onto a real world model of my ‘self’ to load in that particular environment (i.e. one where the room was shaking around), as it had never had any reference data under similar circumstances.

Anyway, this just totally makes me crazy thinking about it. No need to load any data into RAM, no need to boot an OS, just taking in a couple of visual, audio and physical orientation-based input data streams for a half second or so is enough to kickstart those reinforcement learned neural circuits back into firing in recognisable patterns which correlate with wakeful consciousness, self-awareness, and understanding of your environment. You’re back to your normal self. Pretty neat, huh? That’s what a special-purpose processor running a hard-wired algorithm, evolved to be natively efficient, can do for you.

I just think the brain is cool sometimes.

The main premise of the book is how technology will change our identity in the next few decades. Greenfield puts forward three main ‘identity camps’ into which we could possibly fall as individuals: Someone, Nobody, and Anyone. Someone is a Western idealism of ‘making your name’ in society (usually through a career), commanding respect and individuality. Nobody is the scenario where identity becomes consumed, for example by existence in a virtual world, which amounts to living for a ‘here and now’ sensory experience in order to activate the pleasure regions of the brain. It is also related to low amount of personal development, and giving little thought to behaviour which at present may not be pleasurable, but which in the longer term have a higher level of reward. Anyone is the scenario where you take on a group identity via a commonly held set of beliefs, for example where a personality is dominated by a particular political, philosophical or religious movement.

There are many links between these three personality sets, for example the shared feeling of ‘fulfilment’ or ‘meaning in life’ shared by the successful Someone and Anyone, and the ability to absorb new concepts quickly, shared by the Someone and the Nobody.

Each personality is explained by reference to how the brain changes and undergoes reinforcement learning with respect to each possible personality ‘basin of attraction’, so the identities that we assume become part of our neurological wiring. Overall an interesting read, although expect more of a philosophical journey than a book giving an introduction to some of the more scientific details of neuroscience. For that I’m currently reading ‘The 21st Century Brain’ by Steven Rose.

Here is the Promo video:

PROMO VIDEO

And here is the actual lecture:

IET TURING LECTURE 2010

IET/BCS Turing Lecture 2010 – Embracing Uncertainty: The new machine intelligence Professor Christopher Bishop, Chief Research Scientist, Microsoft Research Cambridge Computers From: The IET/BCS Turing Lecture 2010-02-25 00:00:00.0 IT Channel

The lecture was interesting, it focused mainly on Bayesian inference techniques and how they can help us in handling large data sets. Professor Bishop described how Microsoft have incorporated this research into a new tool called Infer.net.

I spoke to Professor Bishop after the lecture, specifically I asked him if these techniques could benefit from massively parallel architectures. He said yes they could. I then tried to ask about whether or not some of these techniques (for example the message passing part of the algorithms – watch the video at around 18:20) could potentially be mapped onto, say, an optimization approach. There is a connection here with Hopfield networks and energy minimization and the like here, but it’s not immediately obvious from the explanations given in the lecture. Unfortunately I wasn’t able to get very far with this discussion as there were lots of other people asking questions too. But it is an interesting train of thought, and as I didn’t want to take up all the speaker’s evening with this line of questioning, I thought I’d probably better buy his book and think over it a bit more instead

Hopefully I’ll put up here any presentations/essays/slides/generally useful information about the stuff that I am interested in and researching, which is basically anything to do with Josephson junction technology, superconducting electronics, experimental quantum computing, quantum neural networks, artificial general intelligence and the brain. I’m currently in the process of getting some new videos edited too, so they will be going on there soon. I’ll put a separate post about those. Until then, you can enjoy perusing slideshow PDFs of several presentations that I have given to a range of audiences.

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Here’s some cake to enjoy with the slides:

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(This one was from our regular Wednesday post-group-meeting ‘cake club’)

Josephson junction simulation of neurons

by Patrick Crotty, Daniel Schult, Ken Segall (Colgate University)

“With the goal of understanding the intricate behavior and dynamics of collections of neurons, we present superconducting circuits containing Josephson junctions that model biologically realistic neurons. These “Josephson junction neurons” reproduce many characteristic behaviors of biological neurons such as action potentials, refractory periods, and firing thresholds. They can be coupled together in ways that mimic electrical and chemical synapses. Using existing fabrication technologies, large interconnected networks of Josephson junction neurons would operate fully in parallel. They would be orders of magnitude faster than both traditional computer simulations and biological neural networks. Josephson junction neurons provide a new tool for exploring long-term large-scale dynamics for networks of neurons.”

Advantages of using RSFQ-style architectures include the non-linear response of the elements and the analogue processing capability which means that you can mimic more ‘logical’ neurons with fewer ‘physical’ elements. I’m pretty sure that this is true. In addition, you can think of other wonderful ideas such as using SQUIDs instead of single junctions (hmm, I wonder where this train of thought might lead) and then apply non-local (or global) magnetic fields to adjust the properties of the neural net. Which might be a bit like adjusting the global values of a particular neurotransmitter.

I’m a bit worried about this approach though. Current superconducting technologies tend to have a low number of wiring layers (<5), and as such are pretty much a 2 dimensional, planar technology. The maximum tiling connectivity you can get from a single layer planar architecture is presumably 6 nearest neighbour unit cell. (Hexagonal close packing). The three dimensional packing in a real brain gives you a higher intrinsic level of connectivity, even though the structure of the neocortex is only quasi-3-dimensional (it is more like 2D sheets crumpled up, but even these '2D' sheets have a fair amount of 3D connectivity when you look closely. In a real brain, each neuron can have tens of thousands of differently weighted inputs (the fan-in problem). Try building that into your mostly-planar circuit

One good thing about using analogue methods is that not all the neurons need to be identical. In fact having a parameter spread in this massively parallel architecture probably doesn't hurt you at all (it might even help). Which is good, as current Josephson junction foundries have issues with parameter spreads in the resulting digital circuitry (they are nowhere near as closely controlled as semiconductor foundries).

The paper claims that the tens of thousands of neurons in a neocortical column might be simulable using this method. I think that with present LSI JJ technology this is very optimistic personally… but even considering the connectivity, parameter spreading and fan-in problems, I think this is a very interesting area to investigate experimentally.

I’ve actually written a bit about this topic before:

Quantum Neural Networks 1 – the Superconducting Neuron model

In that blogpost there were some links to experiments performed on simple Josephson junction neuron circuits in the 1990′s.

Thanks to Dirk for pointing this one out

Natural and artificial atoms for quantum computation

I’m about to give a short talk on this very topic to an undergraduate Computer Science class. The talk will serve two purposes, it will be an introduction to the myriad of different methods by which qubits and quantum computers can actually be realised, and secondly it will be a nice insight into some of the things that experimentalists have to worry about when they are actually building quantum computers. Here is the talk overview:

Models of quantum computation
Implementations
Ion traps – Optical photons / Neutral atoms – NMR – Superconducting circuits – Nanomechanical resonators
Example of operation
The Bloch sphere – The density matrix
Decoherence + limitations
The DiVincenzo criteria – Measuring T1 and T2 – Sources of decoherence

Here are the slides:

Unfortunately I won’t be recording this one so no videos this time. Boo.

Humanity+ UK 2010

Confirmed speakers include Rachel Armstrong, Nick Bostrom, Aubrey de Grey, Max More, David Orban, David Pearce, Anders Sandberg, Amon Twyman, and Natasha Vita-More. It’s a great opportunity for those who are curious about futurism, transhumanism and accelerating technological change to meet and talk to a wide range of people interested in these subjects.

There will be a conference dinner after the event at a nearby restaurant. Visit the website to find out more and register for the event.

The main problem I had during the talk was that I wanted to stand in FRONT of the rather large podium/desk in order to talk to the audience, as I felt this would be a bit more personal (rather than ‘hiding’ behind the desk). However, the controls for the visualiser, (which is a camera pointing at an illuminated surface connected up to the projector so that the audience can look closely at objects you wish to show) were behind the desk, so I had to keep running backwards and forwards every few minutes to switch from visualiser -> laptop output. This was most irritating and is a really poor design in a lecture theatre. The control for the projector output really should have been somewhat more mobile.

The other moment of complete fail was when the large piece of YBCO stubbornly refused to cool to below 90K when immersed in the liquid nitrogen. Stupid smug piece of perovskite. I stood there for what seemed like hours, with over 80 pairs of curious eyes fixated upon my failing experiment, eagerly anticipating some badass superconducting action. And the damn magnet wouldn’t levitate. There was just way too much thermal mass in the YBCO block and its metal/wood housing to cool it quickly enough. I eventually gave up and swapped to the smaller YBCO piece, making some passing comment about physics experiments never working.

Anyway, those gripes over, the talk seemed to attract a lot of questions relating to the last 30% of the material I covered, namely the part about simulating the human brain and potentially building quantum elements into such machine intelligences.

Anyway I hope it inspired some of the younger members of the audience to consider working as scientists in these areas to be interesting career paths.

I’ll try and get the talk edited and put up on the web soon

Non-Abelian geometric phases in ground state Josephson devices

It’s a shame that the proposed experimental scheme is based around charge qubits

EDIT: A couple of other interesting recent Physics stories and links:

Quantum photosynthesis
Spin qubits at Princeton
A Simple n-Dimensional Intrinsically Universal Quantum Cellular Automaton
Fixed-gap adiabatic quantum computation

The Quantum Brain – AMAZON

Satinover explains how quantum processes may underpin the workings of the brain, but not in the usual Penrose interpretation of microtubule activity leading to large scale quantum coherence, but more from a quantum chaos point of view. Satinover argues that quantum chaos can lead to enhanced pattern stability compared to classically chaotic systems, which then persist up to larger scales.

The philosophical implication from these arguments is intriguing: Because quantum mechanics is a non-deterministic process (to the best of our knowledge) then if the brain acts as a quantum amplifier in some way, then it too may also take advantage of this non-determinism.

The idea of a ‘quantum amplifier’ is introduced via our old friend Bob, who cannot decide which of two women to marry. He is struck by the fact that if he is a fully deterministic, mechanistic being, then the person whom he will marry will, in some way, have been preordained. He dislikes this idea, and so bases his choice on the outcome of a quantum mechanical experiment, such as the beam-splitter experiment, whereby a photon has a 50-50 chance of either passing through a half silver mirror or being reflected from it.

In this way the outcome of a quantum process *can* affect the behaviour of a macroscopic system.

Of course this is all very interesting with regards to the question of simulating the human brain at a very low level (one where QM does start to come into play). Just how low is low? Your opinion may change slightly after reading this

These are just a few of the very interesting ideas explored in this book. I’d strongly recommend it.

The Beautiful Brain – A Documentary

The Blue Brain project is an immense undertaking to simulate the neuronal activity within an entire human brain. The project is also looking at the brains of smaller mammals along the way, such as mice and cats.

I’m very excited about this project. Even if it doesn’t yield fruitful ‘simulated’ behaviour in the first instance, I think that it will be invaluable as a resource for other projects. I feel it’s a bit like a connectome project. The map will be useful, even though it still won’t tell us the best way to get from A to B.

The worry I have about systems like this is the absence of interaction with a real environment. I think a brain simulation would need a lot of information from an environment which is similar to that in humans for it to even have a chance at simulating ‘human like’ patterns of behaviour. Additionally, a large proportion of the brain is dedicated to all the regulatory biology of the human body, and processing of sensory input information. What happens to all these inputs, for example the hormone regulation control and the immune system? Are they just to be left open circuit?

The documentary talked about how the plan was to use the simulation to control a virtual mouse or rat body in an artificial environment. In addition to the obvious motor control and sensory inputs, what other parts of the rodent biology will have to go into this simulation?

I guess the question that arises is: Will Blue Brain be given a Blue Body? And what kind of body would be suitable? As far as the senses go, is it easier just to interface with the real world rather than try and simulate an entirely virtual world with exquisitely controlled feedback just to provide the correct inputs for all the I/O systems? In other words, perhaps the simulated brain should be built into a cybernetic organism.

Such an organism would have a completely different ‘biology’ (in fact it wouldn’t be biological at all, but the brain would still need to control complex systems in such an entity) and therefore the brain simulation would have to be grossly hacked to make it compatible. However, this is both more ethical and somewhat easier than, say, cloning an organism without a brain and developing an entire Brain/Body-Computer-Interface just so it can be controlled by the simulation.

The other main thought I have on this is that to simulate a real brain of any kind the structure would have to change in response to new inputs (i.e. learn and form new memories by growing new connections). I wonder if this capability could be built into the simulation. I imagine it must be, otherwise you’d just end up with a purely reactive system, something more akin to a lizard brain with very little adaptive neocortical component.

I wonder how an organism would function if a ‘snapshot’ of it’s brain (including a neocortical component) was run as a simulation, but it was not able to grow any new connections. How would the organism behave? Would it work at all? Is this similar to any existing disabling conditions in humans? Presumably it would not be able to learn, or form new memories.

Any thoughts appreciated.

The text coming out of the wiggly line says ‘You are here’.

Both are very true.

Via Blogging the Singularity

I really enjoyed 2 talks on Thursday afternoon session. The first was by Roderich Moessner and the second by Julia Kempe. They were entitled:

“Random quantum satisfiability: statistical mechanics of disordered quantum optimization” and “A quantum Lovasz Local Lemma” respectively.

I enjoyed these talks because they weren’t completely theoretically based, even though the titles made them sound like they might have been. In particular, I liked the way that random, average and typical instances were considered.

The bounds of ‘hardness’ (going from always satisfiable (easy) to possibly satisfiable (hard) to unsatisfiable (easy)) as you increase the number of clauses compared to the number of variables in a SAT problem were explored, and what kind of phase transitions occur throughout this process. Entanglement can help make some of the possibly satisfiable ones easier, so effectively utilising quantum mechanics allows you to tighten the boundaries of the ‘region of hardness’.

One final thought that I had about the conference was that I think that QIP people need to think about Physics a bit more. Physics seems to underlie all these processes and ties them to the real world in some way. I found that quite a few people were advocating the point of view that Computer Science underlies Physics, but I believe this to be the wrong way of looking at the problem. Physics is all we are given really, it is fruitful to remember this and perhaps just considering it once in a while might help keep you a little more grounded in reality.

Anyway, enough Physics, lets talk about cake. So I mentioned in a previous post about this cake shop I found in Zurich called Cakefriends. Well now I have pictures.

The cake that I chose was a heterostructure of deliciously thick cream (almost cheesecake thick) with interstitial poppy seed sponge layers. To complete the unit cell there was some raspberry sauce around the outside of each sponge layers. It was served in a glass:

Here is a picture of me enjoying said cake. And yes, there were Physics discussions throughout the cakey experience, which should always be the case.

And a photo from the Cakefriends menu:

Yes. Yes we do.

Also thanks to this conference I finally understand the meaning of the complexity class qpoly. Thanks QIP for clearing this one up for me.

On Tuesday the sessions seemed a lot more attuned to the underground QIP physics community. You wouldn’t know we existed by just looking, but we’ve been able to signal our presence to each other by arranging the croissants into Ising configurations.

Anyway, I very much enjoyed the talks by Hari Krovi and Phillipe Corboz. Hari talked about the failure of the Adiabatic algorithm for certain problem instances. This is a very open question and sparked much discussion. My take on this is that most real world problems do not seem to fall into this category, they tend to be somewhat easier. Concentrating on the very hardest instances is useful from a theoretical point of view but not really from a real world applications one.

Kristan Temme’s talk about quantum metropolis sampling was also very interesting. I find myself trying to relate every talk I hear to the adiabatic algorithm. It’s pretty tricky as most of the topics first assume a Universal gate model Quantum Computer, with an extreme amount of error correction. But as I’m interested in actually building Quantum Computers, and I believe that AQC is the best way to achieve this currently, I’m looking for ways to manipulate all these results into a more limited, but realizable framework.

I’m also even posting *this* a day late because just as I was about to make the entry public my internet allocation ran out….

Still, I’ve absorbed a fair amount. Interestingly quite a few of the conference participants seem to be Theoretical Computer Scientists with only very slight inclinations towards the quantum, although I haven’t sampled a large set of conversations yet.

I had lunch with Ed Farhi and several other people working in the area of AQO/AQC. it was really interesting to discuss some of the open questions regarding the adiabatic quantum algorithm.

I really enjoyed Daniel Gottesman’s (Perimeter Institute) talk as he discussed SAT problems in spin systems, which are things that we can actually make and play with, and see if they are behaving quantum mechanically, so I’ll have to talk to people a bit more about that. I’m not sure if there’s any way to distinguish between extremely similar complexity classes such as ‘QMA’ and ‘QMAEXP’ experimentally using such systems, but it might be something worth thinking about.

I also finally met Quantum Moxie.

I really like the way that they have put chocolate bars on all the seats in the lecture theatre…

Anyway, more later.

I’m looking forward to hearing all about the cutting edge of theoretical quantum information! I’ll probably be the only experimentalist there and be totally confused…

Zurich seems very beautiful, I love the river running through the city and there are several buildings with really spikey spires. (A red one and a green one). If I had any sense of culture or history I would tell you all about these buildings. But at the moment I just think they are cool because they are spikey. Pictures and slightly more useful information may follow soon.

There is also a cake shop. It’s called CakeFriends. It’s like they knew I was coming. I tried to get in there today but it was way too busy. Looks like they do really cool coffee and cake Gaining access to this confectionary resource is definitely on the agenda for the upcoming week!

It’s entitled: Quantum Computing – The end of the silicon chip?
For a start that’s a misnomer as Quantum Computing devices are still, for the most part, made on Silicon chips But the idea is that there is a materials revolution in there as well as a shift in computational paradigm.

I want to do a slightly unusual style of lecture where I talk about lots of really cool stuff. I want to get some brains in there somehow so I’m going to talk about the applications of QCs to neural networks. I also want to get in there the idea of how you actually make integrated circuits, what is actually INSIDE your iPhone, and just how awesome the engineering that goes on to produce that kind of thing is. I have a hunch that there’s nothing on the National Curriculum about that kind of stuff. (There certainly wasn’t when I was taught at school). I also want to get some LN2 demos in there as schools always love this kind of stuff.

I’m actually not a great fan of the current demo that I routinely give to audiences of varying sizes. The format generally goes like: Low temp Physics -> Superconductivity -> JJ/SQUIDs -> Quantum Computing.

Why is this bad?

Well, one problem I find with this style of lecture is that you get onto the cool stuff (from my POV) at the end (hell, we make stuff colder than interstellar space and then make it quantum compute. We exploit the power of the multiverse, b*tches!) but in order to get to that bit you have to explain superconductivity, and in order to explain that you first have to talk about lots of low temperature experiments and properties of solids, liquids and gases, blah blah. So what actually happens is that you do all the LN2 demos at the start, and then the audience gets really bored at the end. I also just don’t think that superconductors have the same WOW factor that they used to. I give this lecture so many times and talking about things like High Temperature Superconductivity being cutting edge research just doesn’t do it for schoolkids anymore (it’s also not true). And they’ve all seen the floating magnet and the liquid nitrogen before. It’s sometimes embarrassing…

The second problem is that the EMPHASIS is all wrong. You shouldn’t try to entice kids into Physics by throwing liquid Nitrogen at them, putting balloons and flowers and bananas and *insert your favourite normally-at-room-temperature item here* into cryogenic liquids. It’s quite fun for them to watch at the time, but it’s actually quite psychologically deceitful. Believe it or not, physicists don’t actually dip bananas into cryogens as part of their normal working day.

In fact what we do is even cooler, and getting across a sense of why is much more difficult. But it is also a much more rewarding challenge. So…what I shall try to do is either play down the easy-but-somewhat-irrelevant demos, make the later stuff more awesome, or intersperse the demos through the talk somehow. I suspect I will implement a combination of the latter two.

I also think that these kind of lectures are not supposed to teach kids what we already know about Physics. We should teach them that there’s a lot we don’t know. That is what will probably make them want to be scientists in the future. So explaining the ideal gas law is all very well and good, but they can do that in class. By holding these research lectures, we should inspire and humbly explain that as a scientific community we really don’t know enough, but it’s a great challenge to face that unknown. To teach them that this is where we are stuck, and that’s why we need people like you guys sitting in the audience to ace your science classes now, and help us out in the future.

I’m probably going to blog about the progress of this as I write it. Hey, I might even get some more people attending! I’m thinking of doing a RI Christmas lecture style thing with lots of visuals, demos, audience participation, microscope connected to projector. etc. I’m going to try to get a volunteer to dress up in a cleanroom suit and bring him/her into the lecture theatre to illustrate the idea of humans+fab=bad…any takers?

The bit that struck me the most was how small our sphere of entire radio wave transmission is, compared to the size of the galaxy. Pretty obvious when you think about it but putting it into this visualisation really helps to grasp the sense of scale.

I love the mapping of all the earth’s satellites too.

Disclaimer 1: This is a very general introduction article – it is probably not suitable for QIP scientists who may attempt to dispose of me (probably with giant lasers) for lack of scientific rigor…. *ducks to avoid flying qubits*

Intro
We need to think about what we are trying to build. Say we want to build a brain (in silicon, for arguments sake). Well, for a start that’s not really enough information to get on with the task. What we actually want is a mind in a box. We want it to think, and do human-like things. So we run into a problem here because the mind is a pretty vague and fuzzy concept. So for the purpose of this argument, I’m going to use Penrose’s definition of 4 viewpoints of how the mind might be connected to the physical brain, which is given in his book Shadows of the Mind, but I will summarise here for those who are not familiar with the definitions:

There are basically 4 different ways you can interpret the way the mind is related to the actual signals buzzing around and the physics going on in that wet, squishy 3lb lump that sits in your skull. Here they are:

(A) – The ‘mind’ just comes about through electro-chemical signals in the brain. You could fully reproduce a ‘mind’ in any substrate using standard computer providing you could encode and simulate these signals accurately enough. It would think and be conscious and self-aware in exactly the same way as a human being.

(B) – The workings of the brain can be simulated in exactly the same way as in (A) but it would never be conscious or have self-awareness, it would just be a bunch of signals that ‘seemed’ to be behaving like a human from the outside. It would effectively be a zombie, there would be no ‘mind’ arising from it at all.

(C) – There’s no way you can simulate a mind with a standard computer because there’s some science going on that creates the ‘mind’ that we don’t yet know about (but we might discover it in the future).

(D) – There’s no way you can ever simulate a mind because our minds exist outside the realm of physical science. Period. Even that science which we are yet to discover. (This is a somewhat mystical / spiritual / religious argument).

Interestingly Penrose goes for C – mainly because he believes that there are quantum processes occurring in the brain, and the quantum mechanics going on in there cannot be simulated using a conventional computer. So it’s not that we don’t understand the science yet, but we can’t build computers that are able to take that science into account (i.e. model the quantum mechanics correctly). Or can we… don’t we have, like quantum computers now?

Now back to the quantum braaains…

What do I think is the most exciting prospect for quantum computers? Forget factoring, what about building quantum brains? Note: I’m using the phrase ‘brain’ here in a rather unscientific sense to mean a large collection of interconnected agents – essentially a large neural network.

I am a supporter of (A) – which is a variant of the Strong AI hypothesis. That is, a human-level intelligence could be fully simulated on an alternate substrate using a standard, ‘classical’ computer and actually BE conscious and self-aware. However, with this point of view, one might wonder what a similar level of integration would be capable of if it could use some aspects of quantum mechanics as an integral part of its operation.

My viewpoint conveniently makes my argument for the further development of QCs pretty watertight. If quantum computers ARE required to simulate the human brain, (which I do not believe to be the case), then we should probably develop them anyway. If they are NOT required, but are believed (at least by some) to be fundamentally more efficient for certain computational tasks, then wouldn’t it be a cool experiment to make a brain which could harness that extra computational power? I mean… it would be a fundamentally different type of intelligence. Doesn’t that sound cool? Doesn’t that just make you smile and make the hairs on the back of your neck stand on end? Or maybe that’s just me…

Attentive readers may note that I have subtley disregarded option D here. That’s because D stands for Deepak Chopra, who is much better at explaining how QM ties in with that viewpoint than I am.

Quantum Neural Networks have already been explored theoretically. (See here, here, here for just a taste). I think very small QNNs could be realised experimentally at present. If they can be shown to work in principle, they can be scaled up and investigated further.

Adiabatic Quantum Systems based on the Ising model are perfect for this task. Their structure and behaviour resembles a spin-glass, which is mathematically equivalent to certain types of neural network. A spin glass can store patterns in ‘stable’ configurations of spins, just as the brain stores memories as patterns in configurations of the synaptic strengths between neurons (a simplistic model but it’s kinda the main point).

Of course there’s always the problem of decoherence – and it most likely will be a problem in large scale quantum systems. There’s probably some puddles of coherence around the place, maybe they overlap, maybe they don’t. No-one really knows. Could those puddles of local coherence provide any extra computational power? How connected (or perhaps disconnected) would they have to be? Can we design scalable solid state systems with larger puddles?

Again, that sounds to me like something we should investigate.

In conclusion

We should be able to simulate anything that the brain is doing (even if we need quantum computers). If the brain IS using large scale coherence in its operation, it shows us that it IS possible to build large scale coherent quantum systems (if nature can do it then so can we). This would be useful for all sorts of things, like simulating protein folding. In fact this would arguable be the best outcome. I kinda hope Roger Penrose is right…

However, I don’t believe he is right, as I currently believe the level of large-scale quantum coherent phenomena in the brain is very close to ZERO. But that means we can only IMPROVE the level by which quantum mechanics could be leveraged in brain-like systems, by building huge and densely connected NNs using quantum devices such as superconducting qubits. We can explore completely new territory in the building of intelligent systems…

Thus we have a win-win situation

In other words, QCs are cool and we should build them.
And we need more money *ahem*

Note: I argue this and a bunch of other stuff in my QC & AI lecture. Here is the link to my post about that

Disclaimer 2: This topic has also probably been debated to death and back on various places around the internet but it’s always good to exhume it once more for a guest appearance. In fact if I wasn’t feeling so lazy (and cold, the heating in here appears to be broken at the moment) I might have bothered to dig up some references. It’s also a useful place to send people to if they want to know my point of view on this.

EDIT: To perfectly illustrate both my points that a.) there’s loads of stuff on the internet + I’m lazy and b.) software systems are surprisingly intelligent already (WordPress helpfully pointed out the link for me) here’s some stuff that Geordie wrote about this a while ago:

Can an artificial general intelligence arise from a purely classical software system?

Theory of Macroscopic Quantum Dynamics in High-Tc Josephson Junctions
The Tunneling dynamics in s/d-wave hybrid junctions is difficult due to the non-ideal barrier interface and the unknown effects of quasiparticles. This paper furthers the compilation of a theory to describe these unusual junctions.

High-performance Energy Minimization with Applications to Adiabatic Quantum Computing

Tunneling-induced coherent electron population transfer in an asymmetric quantum well

Phase gate of one qubit simultaneously controlling n qubits in a cavity or coupled to a resonator
- Franco Nori’s latest work.

A Quantum-Bayesian Route to Quantum-State Space

Theory of two-dimensional macroscopic quantum tunneling in a Josephson junction coupled with an LC circuit
-More from Kawabata (and Bauch)

Measurement of the Josephson Junction Phase Qubits by a Microstrip Resonator

Switchable ultrastrong coupling in circuit QED

Quantum Computers

From the website:

“Maybe you’ve never heard of Quantum Computers before and are partial to purchasing from another company that might have advertising everywhere you look.”

Ah, shucks… so that’s where I was going wrong.

A little earlier I nonchalantly and lazily microblogged using the oh-so-professional channel of my Facebook status about something that slightly irritated me whilst watching the news. Here was what I wrote:

One attempted act of terrorism: Almost 24/7 news coverage. Thousands of scientific breakthroughs every day: Not even a few minutes of airtime. No wonder the general public feel isolated from science, in fear of technological advancement and generally depressed at the state of the world…

Anyway, seeing as the comment sparked quite an interesting conversation, I thought I’d relay these thoughts on this, my slightly more traditional soap-box haunt.

Obviously my comment pertained to the recent attempt to bring down a plane travelling from Amsterdam to Detroit. How much about this could there possibly be to report? Hours worth of television time, apparently.

It just depresses me that the news programs, day after day, cover crime, terrorism, war and political unrest as their main stories, then attempt to lighten this doom-riddled cake of hopelessness with a cherry in the form of a ridiculous human factor story about some family’s cat being rescued from a tree (or something equally banal). Earlier today I watched a story about freak weather in the UK – the seemingly important aspect of this being a woman who gave birth in an ambulance because it was stuck in the snow on a sliproad on the A14. Oh, and then there’s 10 minutes of football news!! If I were sat at a desk I would somewhat non-figuratively be slamming my head against it at this point. Luckily for me I’m on a sofa.

I’m not just going to have a long rant here about what I think the news SHOULD be covering, I think that much is pretty obvious from my list of interests ~superconducting flux qubits, yey!~ <-excuse the voices in my head.
Moreover, I'm wondering: What can we actually DO about it?

During my time in a University setting, I think I've only once been invited to a seminar by a person from the Press (in this case it was Radio 4). I think that the press just do not engage enough with scientists. There is so much cool research just waiting to be explored and popularised. There is also a large body of enthusiastic, young PhD students who would be willing to talk about what they are doing, which would not only help popularise their respective subjects, but also break that 'mad professor / scientist' stereotype which seems to still be hanging around a couple hundred years after it was actually representative.

However, until the 'media approaching scientists' kind of thing reaches a critical mass, it will be up to the scientists to shout louder and more ingeniously to make people take note. It will be up to them to chase down the media opportunities.

And I believe that there is a somewhat insidious problem here – primarily that it's not considered a worthwhile activity amongst science/engineering peers to communicate and popularise your research. Carl Sagan et al. are very much the exception rather than the rule. Popularisation is certainly not taught alongside science, or encouraged, even though there are lots of external grants available for this kind of thing. We rely on the rebellious defector amongst academics to propagate the enthusiasm. Instead, we should be supporting those who wish to act as spokespeople for their research.

A lot of media types are also looking for the ‘scare story’ angle. They will try every trick on the book to hype the negative angle of your research, especially of you are working in disruptive or controversial technology areas. I think that academics should be trained to answer media questions a bit like politicians: Get across the positive impact at all costs (short of actually, you know, lying..). And for goodness sake don’t mention Skynet. Or nano greygoo*ahem* tech. It’s ‘submicron’ or ‘molecular’ engineering, guys!

Ultimately, the goverment hands out the money to the funding councils, and therefore if we as scientists aren’t in the forefront of their minds (I mean look, we’re competing with the NHS, the education system, the war in Afghanistan, etc….), if they don’t see our science and go “Wow, you know – that’s not only the future of our country, but it’s actually pretty interesting too”, then funding for our research will indeed be cut.

So go out there and tell people that your research, be it Physics or whatever, is awesome. In any way you can. Ignore anyone who tells you that it isn’t worthwhile. And if you do it right – if you do it *really* right – then those people might just mention it to their friends the next time they have tea and cake

————–

Note: On this topic with a slightly more transhumanist slant, inspired by the conversation, Stuart has also written a blogpost.

Merry Christmas by the way people.

The McGovern Institute for Brain Research at MIT

Click on the link to watch a very inspirational 20 minute video about the research and wider goals of the Institute. Ed Boyen is featured with his Opto-genetic technology. I saw him talk at the Singularity Summit earlier this year. Really interesting stuff. Not only is the science here very impressive, but the way these people respect their colleagues and consider the wider impact of the research to be their keystone is just brilliant.

I love the quote at 17:00:
“We can do absolutely anything here. There are no excuses.”

Adiabatic Cluster State Quantum Computing

Which suggests that it might be possible to implement gates on a suitable AQC architecture (I wonder where we could find one of those?) by slowly changing the overall Hamiltonian of the system to ‘encode’ gates along the way. Measurement is then only required at the end of the computation at the readout stage. Or at least that’s what I think happens

Interesting…

Bio Visions – Harvard

Check out the video ‘The inner life of the cell’.

I probably learnt more about biology in watching this 8 minute video than I would have done in a whole university module. It’s a great project; we need more like it. It got me thinking about visual learning.

Humans seem to be primarily visual animals, and as such we can learn a lot from watching a good visualisation of something. When I read a text book or try to understand a new concept in Physics, I try to mentally imagine a model of the world. I’m using analogies and manipulations of 3-dimensional abstract objects, but I’m always painting a picture. I wonder if having that picture painted for us can help us learn faster.

One could argue that different people learn by different methods, which I’m sure they do. Some people are audio, textual or mathematical learners. But I feel that the standard, classical lecture-style learning in which we are taught most of science is probably also the most inefficient way of getting the information from A to B (i.e. it resonates strongly with very few learners). I think a visual method may not suit everyone, but it would resonate more strongly with a larger number of learners.

In physics, I’ve seen a few 3d visualisations of solid state phenomena, but they certainly aren’t commonplace. I really think that visualisations can help with understanding some pretty abstract concepts, such as the transition of electrons from a classical ‘wave packet’ or ‘particle’ description to a macroscopic quantum wavefunction during a condensation through the critical temperature. Try explaining that to someone who has never encountered any of the concepts. Now imagine that you could project the little video that you run inside your head everytime you visualise the process, and talk through that instead. I’m pretty sure it would make explaining things a lot easier.

There’s a slightly subtle point here: Does the very building of your OWN internal visualisation of an effect help you to truly understand it? If you were just shown someone else’s idea you might not have shared the same thought process and building of the model to get to the final ‘understanding’ stage. Could it even worsen the situation, rendering you biased against developing your own different yet valid visual interpretation?

Is taking away the extra stage of building a world model through internal visualisation really something that would deprive people of insight and deep understanding? Or would that insight just come about all the more quickly?

I’m surprised that the traditional style of lecturing has perservered for so long. One might have thought that powerful and beautiful visualisations of Physics would have pervaded our attempts at conveying a model of the world to students and colleagues.

I think that two hurdles stand in the way.

1.) Existing teachers and lecturers are not accustomed to using new techniques ‘The way you learnt is the way you teach’.

2.) There is an activation barrier to using these new technlogies because they are pretty hard to get going in the first place.

In a subsequent post I might discuss possible ways in which we can do something about this instead of just going off on a complete brain-ramble which is what I seem to have done here

Yes I am hacksawing a dilution refrigerator….

One of the entry ports to the IVC has been hardsoldered with a stainless steel placeholder bush. We need to replace this with our custom made copper bush with feedthroughs for coaxes and DC lines. It is virtually impossible to remove this part given the small space around it, and we decided that we don’t want to put power tools nearby, lest we accidentally buzz through the dilution unit. Which would be a bit like putting a scalpel through the jugular.

So hacksaw it is.

I wonder how many low temperature physicists have wanted to saw their dilution fridges in half before. Today I got to indulge in that pleasure. The results weren’t pretty at times:

Although half way through I started thinking ‘I hope that this is the right part I’m sawing…’

This version is in Spanish, I’m working on getting an English version published but that may take a little while. But the pretty pictures really are the main focus of the book, the text is just an accompaniment, and there are translations available on my website.

I apologise to those readers who have already been inundated with GF paraphernalia.

You can buy the book on Amazon:
Gothic Fall – S Gildert AMAZON

or direct from the publisher (much cheaper):
Gothic Fall – S Gildert NORMA EDITORIAL

Robust Entanglement in Anti-ferromagnetic Heisenberg Chains by Single-spin Optimal Control

Quantum System Identification: Hamiltonian Estimation using Spectral and Bayesian Analysis

Hierarchical Genetic Algorithm Approach to Determine Pulse Sequences in NMR

Efficient creation of multipartite entanglement in flux qubits

Spin Systems and Computational Complexity

Algorithmic Technique for Decomposing Unitary Operations in NMR Quantum Computation

This mask should allow us to do various things. There are some coplanar resonator structures and some time-domain coherence chips here.

I’ve had to put anodisation bridges and anod-layer cuts all over the place. This is because the entire bottom layer of Niobium needs to be anodised anywhere where a junction region is defined. Anodisation requires electrical contact between regions which can be floating (such as where you have qubits). So you must join up these regions to the main bottom wiring layer and then cut them later in the later process step. I really hope I didn’t forget any pieces!

After all that mask designing, some cake is required. Here are some delicious cakes I baked a little while ago:

Tech Know: Fast forward to the past

“The growing number of artists and amateurs who have built steampunk devices has led the Oxford Museum of the History of Science to mount an exhibition of them. The show runs until February 2010.

Browse the exhibits and spend time with steampunks and it becomes obvious how to spot members of that distinguished breed – they are the ones with the swagger and buckets of style”

Here are some more Pretty pictures

There’s also a blog:
Steampunk Art @ Oxford, The Museum of the History of Science, Oxford University

I love working with these guys, they are really good at what they do. Thanks go to all the guys there for making my trip very enjoyable. I played with the latest quantum processors, which gives me a total buzz. I did more Physics than I think I’ve done in months, musing over the Hamiltonians of Ising spin systems and whatnot. The processor technology is really coming along now, as I mentioned a few posts ago.. I totally want a quantum computer for Christmas. Superconducting flux qubits FTW!

Vancouver was cool too. I ate more sushi than I probably should have done. There was lots of rain, but then again I don’t mind the rain, especially when there are accompanying storms. I like Vancouver and the West coast in general as all the people there are so enthusiastic. It beats the perpetually miserable British folks

So although I have been neglecting to post over the last 3 weeks, lament not this absence. There has been tons of cool Physics, lots of fun, and… did I mention… cake. They MADE ME CAKE. Not only can these guys engineer the most advanced superconducting processors in the world, but they also make a mean chocolate cake. Respect. So we had a coffee, cake and donut party I had to honour that on here of course.

Check it out:

Where there are quantum computers, let there also forever be cake…

Specifically, why is drawing lifelife objects difficult, especially from memory?

I like to draw, and I often come across people who say “I wish I had that talent/skill”. I personally don’t believe it is an instrinsic talent, I think that anyone can learn to draw with the appropriate tuition. For the most part it involves following a fairly simple set of rules. Deciding what to draw is the ‘creative’ part When I was originally interested in drawing, I was told by teachers to always ‘draw what you see’. This is an interesting statement, as what we see and how we interpret the information are two very different things.

Try to draw a face without looking at a reference. You’ll get it wrong. I suspect there are only a handful of people that can draw realistic faces without a reference (or without some rule of thumb/mathematically based guidelines). I personally tried to perfect this skill for years, before I actually realised that it was damn near impossible. And it wasn’t a lack of general artistic ‘skill’ – I could draw almost perfectly from a reference.

The problems associated with drawing from memory are to do with our hard-wired vision processing architecture. Our brain interprets data by using heavy compression algorithms. Lines are built up into shapes, shapes into blocks of shapes commonly found together, and these blocks of shapes along with colour information form concepts. Each level of complexity is recognised at a different level in the cortical hieracy, and each level is a compressed version of concepts from the previous one. In other words, our brain works by vector image processing.

So when we hold an ‘idea’ of a picture in our mind, it actually looks nothing like the real-world version. It is very interesting to watch children try to draw – they construct everything out of lines and basic shapes, because this is how the concepts of those objects are stored in the brain.

So how can we break this instinctive behaviour in a bid to become more artistically fluent?

I think that it requires turning off your instinctive mind’s-eye view of an object. There are several ‘tips’ that one can follow to attempt to bypass the brain’s heavy image processing. Most of these are things that each artist personally rediscovers by trial and error over many years.

1.) Draw based on tonality, not shape.
If you see an area of shadow, shade in this region. Try to view the subject as blocks of colour, light and dark areas, not as a predefined ‘object’ such as a bowl of fruit or a face.

2.) Never draw lines!
Lines hardly ever exist in real life. They are created by your brain as a way of compressing information about a step change in brightness, saturation or colour between two areas. If you simply must draw lines, draw them faintly and then come back to it later and match the tone of the line with a filled (crosshatched/shaded, whatever) area on one side.

3.) Turn the reference image upside down.
This technique works amazingly well. Your immediately kill some of the recognition pathways of the brain just by inverting the object. Your brain doesn’t store patterns (concepts) like ‘an upside down horse’ because we don’t often see upside down horses.

4.) Use a ‘window’ referencing technique
Cut a window out of a piece of paper and draw from the reference only what you see through the window (works best with a printed reference image)

5.) Flip the image
If you are working digitally, flip (mirror about x co-ordinate) both your image and the reference image quite often. You often spot mistakes immediately, and this prevents you from becoming ‘accustomed’ to any major errors in perspective/anatomy etc in your piece. You can do the same thing non-digitally as a check if the image is on a single side of paper – by holding it up to the light or using a lightbox. (You can’t edit the other side though, this is a read-only technique!). Interestingly this method works in somewhat of an opposite way to 3.) because the flipped image is (usually) still consistent with reality, therefore you actually use your brain’s ability to pattern match to spot the inconsistencies very quickly, as opposed to reducing the pattern matching it is able to perform by making the image non-realistic.

6.) Observe the image from a greater distance
This allows you to spot certain compositional errors without your brain automatically finding the focal point of the image and ‘ignoring’ the rest, as it does if you are close enough to see the detail.

I guess the GOLDEN rule is to always use references where possible. I had an issue with this for many years as it seemed like ‘cheating’. It’s not cheating, it’s just that our internal references don’t look anything like reality, so if you want to draw something realistically, don’t rely on your brain! (In fact save it for the creative part).

Out of interest my book is now available on Amazon. N.B: This first publication run is IN SPANISH. For translations of the writings accompanying the artwork, you’ll have to visit my website until I get the English version released. The prices and availability also seems to have screwed up a bit. I think these are just teething troubles. I’ll probably put a dedicated post about it when everything has actually been sorted out properly

The fridge in question will be used for several different ‘interchangeable’ experiments, which always makes it tricky to wire. The majority of the lines need to be coaxial, as they will be used for either microwave transmission or fast pulse control of qubits.

What D-Wave are trying to build

The posts are still being added at the moment, so make sure you check back often to look for updates. For my colleagues who read this blog (I know who you are…) you should definitely check it out.

I’m designing some qubit circuits. Process design rules are a pain, there are about 10 layers in a Standard Niobium process and you have to get all the holes and structures spaced correctly (do I hear a tiny violin?). Luckily I have several helpful guides such as Ustinov’s group website, which contains information (mostly in the doctoral theses) on their structures which were fabricated by VTT and HYPRES.

Here are some pictures of what I’m doing:

They are very preliminary designs at the moment, I haven’t even got all the layers in there yet.
I also have to calculate the mutual inductances between the structures using finite element techniques, which lets you know how well your qubit couples to your readout circuitry (In this case, DC SQUIDs and microwave resonators, depending on the design). It’s quite fun to do circuit layout though. These circuits will probably be realised at the European FLUXONICS foundry at IPHT.

Adiabatic quantum computation along quasienergies

A potentially new model of Quantum Computation, which is a discretized variant of Adiabatic Quantum Computation (AQC). Is it equivalent to the standard model? Is it useful? No-one knows.

This paper also got me thinking:

Electronic structure of superposition states in flux qubits.

How do you measure the cattiness of a flux qubit? Cattiness being defined as the ability of a system to exhibit quantum properties as it approaches a classical limit in terms of mass, size, or some other measure. The name comes from the question of whether or not it is possible to put an entire ‘Schrodinger’s cat’ into a macroscopic superposition of states.

I have been wondering about this problem with regards to flux qubits for a while. You might think it is possible just to ‘count’ the number of electrons involved in the Josephson tunneling, giving around 1^10 particles. But wait, the electrons all form a macroscopic state – do you count the condensate as a single particle instead? This paper argues that the actual cat state is somewhere between these two extremes. This is good news, because although the upper bound would have been cooler in terms of Macroscopic Quantum Coherence, the superconducting flux qubit might still be the ‘cattiest thing in town’.

I’m also wondering about the cattiness of nanomechanical resonators coupled to optical or microwave cavities. This system can be put in a superposition of two mechanical states relating to the position and motion of the atoms in the bar. For example, the ground state can be thought of as the fundamental harmonic of the bar (think of it like a guitar string), with an antinode in the centre, wheras the first excited state has a node in the centre and two antinodes at 1/4 and 3/4 of the way along the bar. But here we find a similar problem to that of the flux qubit: Does the number of atoms in the bar matter?

For fun let’s calculate the number of atoms in a Niobium nanomechanical resonator:

Let’s say the mechanical bar is 20nm x 20nm x 1um.
The volume of the bar is therefore 4e-22m^3
The density of Nb is 8.57g/cm^3
The mass of the bar is therefore 3.428e-17kg
The atomic mass of Niobium is 92.906amu = 1.54e-25kg.
The number of atoms in the bar is ~2.2e8

To check that value:
The atomic radius of a Nb atom: 142.9pm = 0.1429nm
In 20nm there are 139.958 atoms,
and in 1um there are 6997.9 atoms.
Therefore in the bar there are 1.37e8 atoms

which is roughly the same as by the previous method.

So does that mean the ‘cattiness of the bar’ has an upper bound of 2e8? This would make it more catty than the flux qubit. Or do you have to assign more (or less) than one ‘quantum degree of freedom’ per atom? It’s not as simple as tunneling electrons, where the quantum state is determined by the direction of current flow around the loop. If anyone has any thoughts on this they would be appreciated. Just what exactly are the quantum degrees of freedom here?

The bar is obviously constrained by its end points, albeit not ideally. The displacement of the bar may therefore probably behave more classically near the ends, or the wavefunction may extend into the structural supporting region. This may affect the actual number of atoms in the superposition. What fraction of the length of the bar is behaving quantum mechanically?

Note that the mass of both the electron condensate in the case of the flux qubit AND that of the nanomechanical bar are both much lower than Penrose’s quantum mass limit of about 1e-8kg – so we can’t test that hypothesis in the lab yet. Note this relates to a post I wrote a while ago about electrons in a lump of superconductor – there are enough electrons in a bulk sample for the mass to be greater than the Penrose limit, but they aren’t doing any useful quantum computation, you can’t put them into a well defined superposition of states for example. We need to ENGINEER and CONTROL these cat states…

Anyhow, after that complicated Physics we are definitely in need of some cake:

We had this type of cake yesterday (amongst others) to celebrate a colleague passing his PhD viva

Superconductor World Record Surpasses 250K

Here is the YouTube video – my talk starts at 5:15 minutes in. You can get to the other half of the talk (and the rest of the talks at the meeting) via this link. I’m sandwiched between Anders Sandberg and Aubrey de Grey…

The talk was basically about two things: Online tools for collaboration between research scientists and research groups, and crowdsourcing the potential of the general public for the advancement of science. It was quite a short introduction but I think it got the point across and it got people thinking. I’ve added Mechanical Turk / Clickworkers / several scientific social networking sites to my list of crowdsourcing ideas since giving the talk (which centred around the examples of Galaxy Zoo, BOINC, the Polymath project and Wikipedia/Scholarpedia), but if anyone knows of any more I’d love to hear about them.

Here are a couple of pictures from the talk:

Photo credit to Kopacetic

However there is one problem. As I’m walking through campus, I keep thinking I’m in Second Life. In SL, people convey information on these giant boards that look identical to these ones… apart from they take longer to load in SL. Unless I haven’t had enough coffee in the mornings, then it’s a pretty close call. So you see, I’m now having even more difficulty separating my real and virtual lives…

In SL, you can click on the boards and they give you information. I’m wondering if we can write an iPhone app with one of the augmented reality frameworks that does the same thing with these boards. Perhaps it could take you to the group’s website, etc…

And yes, the students do behave in a remarkably similar fashion to SL avatars. Sometimes they just stand around like they are totally AFK.

I was thinking after the LHC rap that a QC rap video would be cool to do, but I don’t think anything I could do could possibly match the standards set by the Australian Commonwealth Scientific and Industrial Research Organization (CSIRO):

I also now want a giant gold medallion with a on it…

Ray Kurzweil addressed the Singularity Summit on Sunday and gave a brief summary of his opinions on some of the other preceding talks. He specifically answered criticism from others of our ability to ever model the brain using classical computing due to the presence of quantum effects in the brain. I don’t know of any supporters of this hypothesis other than Penrose and Stuart Hameroff, but maybe they are out there. He supported his viewpoint by saying that ‘The brain doesn’t factor large numbers’.

I agree with the statement that the brain is not necessarily ‘quantum computing’, but I disagree with this particular argument, because the brain does do lots of other things which quantum computers might ALSO be good at, such as pattern recognition, image processing and memory retrieval (database searching). So I think any argument as to why the brain isn’t quantum computing needs to be a bit more watertight (start by explaining decoherence for example) if you’re going to tackle this issue.

As a secondary effect, it perpetuates the myth that factoring is the only thing QCs will ever be used for. Which is sad, because a lot of smart people might have taken that impression away with them.

An introduction to measurement based quantum computing:
Measurement-based quantum computation

Preprint of the latest offering from Martinis’ group showing tomography of two entangled gates after performing various operations:
Quantum Process Tomography of a Universal Entangling Gate Implemented with Josephson Phase Qubits
It’s amazing what you can demonstrate with just 2 qubits.

The published version of Seth Lloyd’s Quantum Algorithm for linear systems of equations:
Quantum Algorithm for Linear Systems of Equations
(and here’s the preprint of that one).

…and here is the link:

Singularity Summit 2009: The Faces of Singularity – Pop Sci article October 2009

I am number 6 of 7 in the line-up of photos. It’s an absolutely terrible photo, but I wasn’t feeling 100% that day, and I got the camera shoved *right* in my face!

I don’t know if it’s actually in the real paper mag or not; I spent a long time at JFK looking for the fabled hardcopy of H+ magazine (which I didn’t find btw) and didn’t take the time to look through the Popular Science mag (or buy a copy). Now I’m in the UK and it looks like it isn’t stocked over here! If any of you guys across the pond sees a copy in Hudson’s, have a flick through for me please!

Interesting that they show a 50-50 m-f ratio in the interviews. I wonder about the randomness of their sampling strategy, heh

Everyone seemed to be very excited about being here. I’ve never felt such a buzz before from attending a conference. In addition, everyone seemed interested in everyone else, which is certainly not usually the case at Physics conferences

SS seemed to be a place where ideas could be generated too. It seemed that immersing people in an environment where they talk about AI, human enhancement, robotic cars, longevity and anti-aging research allowed ideas to cross the conventional bounds of these individual fields.

Peter Thiel gave an inspiring and motivational presentation about how we need to be doing more, and faster, to maintain economic growth as we head towards the SIngularity. Along with David Rose and Mark Gorenberg, he also explained how Venture Capital could possibly help, and addressed some of the difficulties entrepreneurs have in finding VC funding for Singularity-based business ideas, and indeed what something like a Singularity might mean for investment and growth.

I find it vaguely amusing that business people appear to view the Singularity as a more serious idea than most scientists do

Robin Hanson also discussed similar concepts in his talk, where he explained that the driving goal of academics is to affiliate with certified ‘impressive’ people rather than to further human knowledge, and described how this effect led to progress toward the Singularity being driven by tech and business rather than scientific research.
Another highlight of the day was Eliezer Yudkowsky tying people’s brains in knots again with an enlightening talk about our neglect of scope with respect to existential risk, and how we must be wary of conjunction fallacies and the ‘bystander effect’.

Some talks, video clips and other bits from SS are available here, I think it’s great for them to be available for anyone to watch.

The Singularity Summit 2009 opened in the small but perfectly formed Kaufman Concert Hall, 92nd Street, New York, NY. I say it was small because fitting 800+ Singularitarians in there was a bit of a squeeze, especially during the coffee breaks when everyone left the main lecture hall!

After an introduction by Michael Vassar, we had a talk by Anna Salamon about the intelligence explosion, and Anders Sandberg gave a nice introduction to whole brain emulation. Randal Koene talked about why we should get our asses in gear with the whole brain emulation stuff because there are plenty of reasons why humans could become extinct without much warning. Itamar Arel explained that we already have all the pieces we need for AGI to be realised.

Ben Goertzel talked about OpenCog and AGI, Stuart Hameroff gave a very rapid dash through many different aspects of his quantum consciousness theory, Michael Nielsen gave a talk about quantum computing – (it was cool to meet Mike of Mike&Ike fame ) and Ned Seeman talked about the awesomeness of DNA.

Lunch was an interesting combination of samosas, coffee, carrot sticks and potato chips. I also purchased an SIAI t-shirt which I subsequently discovered is way too big for me, but it was the smallest size they had!

After lunch and associated networking we had a talk from Jurgen Schmidhuber (which was as entertaining as it was enlightening) about human creativity, self-referential Godel machines and a mathematical foundation of creativity. Stephen Wolfram then had a kind of ‘discussion session’ rather than a presentation, which mainly focused on mining the universe of computational possibilities for interesting programs. David Chalmers talked about AI, AI+ and AI++, simulated evolution and a ‘leakproof’ simulated world, and Gary Drescher talked us through Newcome’s paradox and the prisoner’s dilemna. Ed Boyden gave a fascinating talk on synthetic neurobiology including fibre-optic control of neurons, and Marcus Hutter talked about combining the ideas of Ockham, Bayes, Turing and Epicurus to form the mathematical foundations of AGI. William Dickens gave a talk about whether or not IQ tests really measure intelligence, and finally Ray Kurzweil talked about the law of accelerating returns and gave some comments on the preceding talks.

The coolest thing about the summit is that the people in the room are most probably the very same people that will drive the singularity to actually happen. I personally find it quite moving to be in a room with 800 or so people that share the same philosophy and ideals as myself.

We finished off the day with drinks and interesting conversation amongst some of the SENS people, and Aubrey de Grey.

Looking forward to tomorrow’s schedule – this is much more exciting than Physics conferencing

We also had workshop sessions on IP, the patent system, commercializing research, licensing, spin-off companies, and we had several presentations on case studies of companies (mainly with a biotech theme). The course of patents was incredibly interesting, it taught me not to give away any really good ideas at conferences etc., as the information would then be in the public domain and impossible to patent.

Is it important for academics to learn about business? I would say yes. I think it’s a great idea to see your research being put into application and practice. It’s also good to talk to people from industry, they have a vastly different point of view which can make you reconsider your work in a new light and with a more open mind. I’d recommend this kind of course to anyone. Even if the material hadn’t been up to scratch (which was not the case here), I met some very interesting people and made useful contacts with people of a similar mindset but working in diverse disciplines.

Physics is a little more difficult to commercialize than biotech and chemical engineering, but there are still opportunities there and we shouldn’t let them slip by because of some ancient ivory tower stigma.

By the way I’m now in New York but more on that later….

(Via Bad Astronomy)

“The sky calls to us
If we do not destroy ourselves
We will one day venture to the stars”

Almost makes me cry… Here’s to the amazing Carl Sagan.

Violation of Bell’s inequality in Josephson phase qubits

I was wondering if you could demonstrate this the other day!

From Science Daily:

The measurement of a Bell violation in a superconducting circuit was recently stated to be the next primary challenge for the superconducting qubit community, according to Martinis.

Martinis said: “This experiment has met this challenge, achieved by performing a very demanding measurement on a pair of Josephson qubits, a measurement that requires excellent control over qubit state preparation, qubit entanglement, and very high fidelity single-shot state measurements of the entangled qubits. It directly proves that quantum mechanics is the only possible description for the behavior of a macroscopic electrical circuit.”

What an AWESOME idea for a conference!

Coherence in Superconducting Qubits 2010

…I shouldn’t get so excited about these things, but, but… just look at the topic list!

This conference will foster discussion and collaboration among investigators stimulating:

* A fuller, more accurate understanding of the types of defects limiting coherence in superconducting JJ qubits; including the mechanisms by which they occur, how they are created in fabrication, the mechanisms by which they affect and interact with the qubit, and the qubit performance characteristics they limit (coherence times T1 and T2, Rabi contrast, etc.),
* Development of advanced methods for definitive characterization and measurement of defects in the presence of other defects;
* Discovery of new low-defect materials and constructions, and the means for their fabrication;
* Understanding of ultimate, coherent qubit geometries begged by the physics, and the means for their fabrication.
* Improvements in electronics and systems enabling high-fidelity qubit control and measurement; and
* Enhanced scientific vision for the most effective means to substantially extend coherence

Ooooh, nice. Hope I can find the money for this. Singularity Summit 2009 might just have me bankrupt!

“Real Lives and White Lies in the Funding of Scientific Research”

An excellent article explaining the problems faced by research groups, especially small ones, when a large portion of allocated researcher time is dedicated to writing grant proposals.

Why not join me for some unmissable Physics action – just click on the link above to sign up. I’ll hopefully by then have some form of poster with results from measurements on Ferromagnetic Josephson Junctions. By the way the abstract deadline is in 2 days time…

You can read about it here:
Scalable ion traps for quantum information processing

Here is the PhysicsWorld web version of the article:
Tiny device is first complete ‘quantum computer’

Haven’t superconducting flux/charge qubits have been able to do this for ages – what’s all this ‘first’ business? Besides, didn’t Yale do something similar a few months back? Oh no, wait a minute, they actually ran an algorithm…
And that’s without even considering the advances in AQC, hmmm.

Anyway, that didn’t bother me too much. But then the article goes on to say:

“The implication was that quantum computers could operate at ultra-high speeds, which could be applied to solving complex problems like cracking some of today’s most widely used encryption codes.”

Is that the BEST application you can think of to run on a QC? Really? Come on… Get creative! Who cares about cracking RSA anyway? They’ll just keep adding more digits

In fairness the magazine also had quite a nice article about the violation of Bell’s inequalities and some of the potential loopholes in those experiments. Which made me happier.

Via Next Big Future: A great video about the progress being made by General Fusion on their hydraulic-based fusion reactor.

Warning: The talk is over 100 minutes long and contains intense scenes of metaphysical speculation, which may not be suitable for children (or the QIP community).

Here’s a nice picture from the talk:

It was actually quite a difficult talk to present, as the audience was extremely broad. I had some people tell me that they’d really enjoyed the Physics section, and others that they hadn’t been able to follow any of that part but that it sounded good anyway. There were over 40 people turned up (I don’t know the exact number), which is good as it means that the meetings are becoming more popular.

But I do have a picture of our cunning solution:

A nifty set of bellows made from the flexible hose stuff that you attach to tumble dryers and some nice adapters to mount it onto the fridge fittings, courtesy of our technicians. I take no responsibility for the crazy MacGyver-ness this time, it was all my colleagues’ doing

(You can actually buy vacuum bellows from places like Kurt J. Lesker, but they cost hundreds of pounds, and this would probably have been specified as a custom job anyway, which can sometimes increase the price by an order of magnitude).

Anyway, the hose confines the boiling gas as you lower the insert. Which seems to work rather well. It also adds a nice steampunk touch to an otherwise commercial fridge assembly.

The talk will be about quantum computing: What quantum computers can do, and more importantly what they CAN’T do, how to build them and how they might be useful in several areas of accelerating technologies. The talk will also address some of the interesting debate around the role of quantum mechanics in consciousness and how this may have consequences in the creation of human-level artificial intelligence.

I’m expecting some lively discussion regarding the final points!

Here is some info on the talk:
(Posted in several places so I’ll link them all)

Extrobritannia
Humanity+
Facebook event
London Futurists forum

If you would like to attend please feel free – follow the links above to find times and places

Towards Quantum Superposition of Living Organisms

The authors describe an experimental proposal involving the placement of a macroscopic object such as a virus in a quantum superposition of states. The object would be held in isolation from the environment using an optical tweezer setup by taking advantage of the dielectric properties of the objects to levitate and confine it in the optical cavity.

Because they are not in contact with a substrate, the objects are able to be laser cooled to the (quantum) ground state of their mechanical motion. The objects can then be excited into a resonant quantum state of mechanical motion, and therefore also potentially into a superposition of ground and excited states.

If you are obsessed with the idea of putting macroscopic objects into a superposition of states (as I am), this article is worth a read

And of course it digs up all those lovely arguments about how macroscopic an organism has to be before it can be considered ‘living’ or ‘self-aware’ etc etc, and once again stomps all over that delightfully metaphysical hallowed ground, whilst physicists run towards their decoherence bunkers in fear.

check out @physicsandcake on Twitter!

I won’t be neglecting the blog, but twitter seems more useful for posting links as you find them. It stops them from clogging up my bookmarks folder!

Yes, that is an icicle on the safety overpressure valve. No, it shouldn’t be there…

A really interesting company I learnt about recently:
Evolved Machines

Upcoming Events

World Future Society conference 8-10 July 2010, Massachusetts
World Future 2010

SENS4 Conference in Cambridge, UK 4-7 Sept 2009
SENS4

and From the RI events calendar:
A brain for life: The 21st century mind Friday 25 September 2009

This one looked good too:
September09 Cafe Scientifique: Quantum mechanics Monday 21 September 2009
but it’s fully booked now. It might be worth calling them a couple of days before to see if there are any cancellations.

If Quantum-themed cafe scientifiques are this popular, I might see if I can get one arranged for Birmingham and get some guest speakers in

Fun and educational videos:

This is awesome:
Scott Aaronson vs. Eliezer Yudkowsky on BloggingHeads TV
Great if you love argumentss about Many-Worlds.

A video feed dedicated to Electronic Engeneering! Brilliant stuff:
Electronics Engineering Video Blog Podcast

‘South Park meets Harvard Business School’

What an experience. The people here are great, real leaders and entrepreneurs. It’s been inspiring to talk to everyone. The format of the course is several weeks of lectures and presentations by distinguished speakers, site visits to companies and R&D labs, followed by several weeks where the students focus on their team projects, with a view to ‘Changing the lives of a billion people’. Several of the team projects are expected to flourish into companies continuing well after SU 2009 has finished.

The course takes place at the same time and place as the International Space University, so there is even more opportunity for networking with like-minded people. In fact SU was modelled on the successful courses that ISU has been running for 20 years.

I’ve mainly been talking to people about quantum computing and future quantum technologies. Everyone has been very receptive and the discussions have been heated and immersive. We’ve talked about everything from uploading to teleportation, and from solving world poverty to finding personal happiness. I’ve also met lots of fellow Singularitarians, and some fellow Transhumanists, which has also helped spark interesting conversation. It’s quite easy to sometimes feel the Loneliness of the Singularitarian scenario that Ray Kurzweil describes, but places like this make you realise that progress IS being made and that there ARE people who share your views, dreams, and goals.

One thing I’ve also learnt here is that people value art, creativity and entertainment as highly as business, tech and hard science. Silicon Valley defintely has a ‘Work hard, Play hard’ mentality. I’m hoping I can take some of these skills back and use them in the somewhat more restrained and introvert world of academic research.

“Emulation of a Quantum Spin with a Superconducting Phase Qudit”
Matthew Neeley,1 Markus Ansmann,1 Radoslaw C. Bialczak,1 Max Hofheinz,1 Erik Lucero,1 Aaron D. O’Connell,1 Daniel Sank,1 Haohua Wang,1 James Wenner,1 Andrew N. Cleland,1 Michael R. Geller,2 John M. Martinis1,

They use a standard type of phase qubit experiment in which the quantum states are defiined by a ladder of energy levels in a single potential well, rather than the more common flux qubit where the energy levels arise from the degeneracy between two adjacent wells. The qubits/qudits are controlled by applying very careful timed and shaped pulses of microwaves to excite the quantum states between levels and to allow the levels to interact with one another. The energy between each level is very slightly different due to the anharmonicity of the Josephson junction’s energy landscape, so each level transition has a unique ‘frequency signature’. (This scheme wouldn’t work if the quantum states were in a harmonic oscillator, as all the levels would be equally spaced.)

The group demonstrate a shift in the Ramsey fringes which equates to the expected Berry phase produced by the rotation of the quantum state around the Bloch Sphere. They also demonstrate robust Rabi oscillations and the swapping of quantum information between states. The relaxation times of the states are all in the 100′s of ns, but are smaller for the higher states.

I can’t really stress how exciting this result is for experimental quantum computation with superconducting circuits – it opens up new possibilities for implementation of algorithms and quantum simulation. The group focus on the potential of the technique for emulating quantum spin systems.

I wonder what algorithms have been developed that require multi-dimensional Hilbert spaces for their implementation? I know that higher dimensional quantum bits can help make quantum cryptography more secure. I find a sudden renewed interest in learning about qudits…

I’m also slightly smug in my opinion that this once again puts Josephson SC qubits marginally ahead in the awesomeness stakes. (Those ion trap guys were really giving us a run for our money). Qutrit systems (3 levels) have been realised in NMR and Ion trap QC but I believe that experiments like this really open up the door for more complex QIP realizations.

Via Academic Earth:

Who needs Harvard? How Web-Savvy Edupunks Are Transforming American Higher Education

Who’d have thought education and anarchy would join forces?

I’m going to have to put together a lengthy list of online resources for this kind of thing at some point. Although there’s plenty of raw material about, I don’t think there’s enough stuff about learning how to learn via this DIY method. That’s why we’ll still continue to pay vast amounts for education, it’s about the framework and technique more than the raw information. I’m currently trying to write something approaching a coherent article/essay about some of these ideas.

Argh – another ‘I’ll write a post’ post – I suck.

Reliable quantum operations performed on an ion trap QC:
Complete Methods Set for Scalable Ion Trap Quantum Information Processing
Here’s a Science Daily writeup of the article

More work towards scaling up macroscopic quantum behaviour:
Observation of strong coupling between a micromechanical resonator and an optical cavity field

Error correcting codes are robust against errors!
Thresholds for Topological Codes in the Presence of Loss

Other cool stuff

Via Next big future: Synapse Project to Make a Artificial Human Brain Gets $16 million more from DARPA More progress towards whole brain emulation….

Via Singularity Hub: Stainless steel printing Make whatever you can design in a CAD program into a metallic meatspace reality!

Via WIRED: Clever Crows Prove Aesop’s Fable Is More Than Fiction
I love crows, and enjoy reading about experiments to determine their levels of intelligence. Here the crows solve a simple puzzle by placing stones in a tube of water to raise the water level in order to obtain a floating snack. Watch the videos, they are very impressive.

Just for fun

Tron legacy concept art takes you inside cyberspace If you like TRON, you will want to check out this concept art for the new movie, TRON LEGACY (IMDB reference here)

Virtual Worlds May Be the Future Setting of Scientific Collaboration

Image © MICA

Could be quite cool if combined with something like Google Wave so you could have avatars inside a virtual meeting room as a front-end for the wave tools.

I’ve been avoiding second life for so long, but I know I’m going to give in at some point… This kind of thing doesn’t help!

I run the fridge about 2 times a month, and I use about 150L of LHe4 each time. Liquid Helium costs about £5 a litre. I also run the Continuous Flow 4.2K experiment quite often, which is about another 50-100L per month. That’s almost £2000 a month just on Helium for 2 experiments. This is why I wish I was a theorist sometimes. I could buy a really neat pencil and some very executive ‘backs of envelopes’ for that. j/k of course

But you can start to see where all the money goes. Helium is usually considered before any of the equipment, maintenance, and technician costs…

If you’ve ever wondered what 150L of Liquid Helium looks like, wonder no more!
It looks like this:

MIT Technology Review on mining data from social networks.
Interesting because I mentioned this a while back

Progress towards a tiny robot that can crawl through the human body

and just for fun…

If you are a total cable geek like me, you’ll love this card game:
AEI Cable Trumps

The list of speakers looks something like this:

Randal Koene, Itamar Arel, Ben Goertzel, Stuart Hameroff, Ned Seeman, Jürgen Schmidhuber, Gary Drescher, David Chalmers, Ed Boyden, Marcus Hutter, William Dickens, Béla Nagy, Gary Marcus, Gary Wolf, Michael Nielsen, Robin Hanson, Brad Templeton, Ray Kurzweil, Anders Sandberg, Peter Thiel
Eliezer Yudkowsky, Aubrey de Grey.

The link also takes you to the website where you can find out more information on this unusual yet inspiring conference.

See you there!

The answer to this is almost definitely yes. Other than it taking a long time, there aren’t really any disadvantages to doing so. It would mean I had access to all my experimental settings in digital form, and I would have a built in cross-reference of the plots that are currently stuck in the lab books and also on my hard drive with the experimental notes (I already save the copies of the plots with the date but then you have to go and get the lab book to look up the date and find what sample the data was from and the experimental settings for that date).

In addition, if I decide to go completely Open Notebook, I could put the digital versions online. Although I’d need a lot of server space for this – Rob, I’m looking at you It would also be cool to build the digital lab books into an Experimental Physics Wiki I’ve been meaning to get around to writing. I could easily navigate around previous experiments by tag too, rather than my current, somewhat prehistoric, method of ‘post-it notes’ stuck to various pages.

Having a digital backup would also put my mind somewhat to rest over the matter that if I lost my lab books I would be totally screwed. All my data analysis and experimental development information would all be lost, and you can’t plot the data without the precious settings which are meticulously noted down in the lab book everytime the experiment is run.

I’m also quite proud of my lab books as they are quite neat and pretty, and are often used to show students a ‘good example’ so it would be a shame to lose them.

So, I was trying to work out what method to use. At first I thought about scanning the books, but I think this will take too long. I have about 8 books with 100+ pages in each. I thought about photocpoying the pages (our photocopier has a function that allows you to e-mail the copy to yourself) but my current favourite is the idea of taking hi-res digital photographs of each page. If I could set up a little ‘photo-shoot’ with good lighting and camera tripod, and a stand for the books, I could probably get them done pretty fast.

I’ve also always wanted an excuse to buy a lectern. Like this one:

An investigation (and rebuttal) of claims that it might be possible to interpret a component of the spectral noise in Josephson junctions as being due to the prescence of vacuum fluctuations:

Dark energy and Josephson junctions

Quite interesting…

The first point I’d like to address is the Superconducting Neuron model – this is an approach which attempts to build real life biologically inspired neural nets from superconducting hardware. I’ll discuss some other approaches to utilising the ‘quantum’ aspect of QNNs more efficiently in subsequent posts, for now this discussion is limited to this one hardware model.

Here are some papers I’ve been reading on the subject:

Mizugaki et al., IEEE Trans. Appl. Supercond., 4, (1), 1994

Rippert et al., IEEE Trans, Appl. Supercond., 7, (2), 1997

Hidaka et al., Supercond. Sci. Technol., 4 (654-657), 1991

There are several advantages to using SC hardware to build NNs. The RSFQ framework makes it much easier to implement, for example, fan-in and fan-out systems. Flux pulses can correspond directly to nerve-firings. The circuit elements dissipate much less power than their silicon counterparts. And you could simulate factors such as neurotransmitter levels and polarity using flux-couplers and bias leads, which (I believe) seems to be a much more natural way of doing things than trying to invent a way to mimic this in semiconductor technology.

What I understand about this field so far: In the 1990′s a couple of Japanese groups tried to demonstrate principles of superconducting neuron circuits. They built a few, and they even worked up to a point. So what has happened to this research?

Four Problems

1.) Well one school of thought is that the device tolerance is just not up to scratch. It is true that when you make Josephson junction circuits, the tolerances on the individual components tends not to be better than ~5%. However, is this really a problem? I can’t see that being the case, I’m sure that the similarity between biological neurons can’t be that good.

2.) Another potential problem is that research into neural networks generally has diminished (partly due to the so-called AI winter). If people using supercomputers can’t get their simulated neural networks to do anything *that* interesting, why bother with building the things in hardware? Such realizations would have far fewer neurons anyway! I guess the answer is that simulating superconducting circuits is still quite hard, and there could be some real advantages to building the things – similar to the reasons for building modern ASICs.

3.) A third problem is device integration level. Even with the best fab facilities available, superconducting circuits can only be made to low level VLSI (10,000′s of junctions). Again my point is – well why not try something on this scale? Unfortunately, cell libraries for RSFQ design probably don’t natively support the kind of realisations you need to build superconducting neurons. (For example, you need a great deal of FAN-IN FAN-OUT). So you’d probably have to go fully custom, but that’s just a design challenge.

4.) And then there’s a theoretical problem that has been bugging me for a while now. Although you can simulate any level of connectivity in highly abstracted models of NNs (given enough processing power and memory), if you actually want to build one, are you limited by the current 2-dimensional planar nature of the fabrication process? In a 3-dimensional interconnected system such as a real human brain, you are able to connect distant regions via UNIQUE, DIRECT links. In a 2D system, you are limited by the circuit layout and can (essentially) only make nearest neighbour connections. I’m pretty sure there’s a graph theory proof pinging somewhere around the edge of my mind here about connectivity in different-dimensional systems. The question is, does this limitation mean that it is theoretically impossible to build biologically inspired neural networks in planar hardware?

The field of RSFQ / Superconducting digital electronics is suffering low funding at the moment from ‘lack of applications’ syndrome. The number of people investigating applications of RSFQ circuits and Josephson logic seems to be much lower than the number of people working on the fundamental Physics of the devices. It’s a problem with the way research is funded. No-one will fund mid-term technology development, it’s either fundamental Physics or applications breakthroughs.

There may well be research being done in this area that I am unaware of, and I would be most intrigued to learn of any progress, and whether there are there problems in addition to the four presented here. However, if the research is not being done, why not? And would it be possible to get funding for projects in this area…

However, I still have several reservations about using third party software for this task, similar to the objections raised here. I worry about loss of data, I worry about future-proofing, and I worry about privacy.

I’d like a system that had some kind of intuitive support for importing and exporting blog entries. I’d like a system that could export the lifelog as a database structured similarly to a filesystem, for example containing only .txt files and .jpeg files. Until I find such a system I think I’m going to try and make my own. I’m thinking of basing it on html and running it something like a wiki. My own ‘LifeWiki’.

I’ve had bad experiences with LiveJournal in the past, for example without a premium member subscription you can’t do any advanced searching, sorting or filtering of the entries. So one of the first things I’ll have to do do is to pull all my old LJ entries and get them into a much simpler LifeWiki thing.

Another good reason for taking this approach is that (unless I wish it otherwise) the data will all be stored on my personal space and I am wholly responsible for backing up. The data format will (at least in some way) be futureproofed. Again it’s up to me to convert the data into new formats, but if the system is kept simple this should not be a problem, and if I am used to managing the system I will be expecting to have to do this. I don’t like the idea of the format of all my data being at the whim of a third party.

Obviously the down side of this endeavour is that LifeWiki won’t have any of the advanced options, it won’t have graphic text recognition searching, it won’t natively support tagging (I’ll have to sort out my own system for that), and it might just end up getting too big to handle in this primitive way.

RedNotebook seems to be the kind of thing I might be looking for, but I’ll have to read more about this and other options. But I’ve always been good at organising things, so it might just be worth a stab at doing my own version for now.

Ingredients: Cream, milk or that strange stuff we don’t have over here, sugar, crushed fruit, and liquid nitrogen. Recipe: Stir the sugar into the cream until it dissolves, then beat it with a whisk until it is light and fluffy. Then add the fruit, mix it in, and then add the LN2, stirring continously until it has hardened. It takes about 5 minutes, as opposed to waiting for it to cool in the freezer for hours. You can use chocolate chips too but for some reason the fruit one seemed to come out better.

Because you have to keep stirring it, it tends to come out in fluffy bits rather than scoopable ice cream, but you can still pack it into a bowl or cone the same way

Rather tasty too. You have to be careful when you eat it, sometimes you get a REALLY cold bit in the middle and a rather bad Ice Cream Headache
To keep it from melting on a hot summer’s day, just add more LN2 topping:

Scientists Create Artificial Brain 7/13/2009

Meet Blue Brain, a “brain” made up entirely of silicon and housed inside an IBM supercomputer. An astonishing advance, the artificial brain may be the first step toward manmade higher behavior, WSJ’s Gautam Naik reports.

This is a really exciting area. The rate of progress of this project is crazy, even with a standard level of funding and linear projections. Just imagine what they could be doing in 10 years time with *more* funding and perhaps unforeseen breakthroughs in HPC.

For more information, BlueBrain homepage is here.

The paper is about using the experimental technique of switching measurements (aka Macroscopic Quantum Tunneling) to assess the quality of Josephson junctions for qubit applications (I can’t give too much away though).

It’s quite frustrating though: Just silly things like ordering of sections, what concepts to introduce in what order, which figures to put in, how to present them (that’s actually not too hard to work out, you just look at similar things in the literature), and how mathematical to be when you are describing the model to fit to the data, and pulling together several ideas from the literature to support your work (this is the hardest bit).

There’s also the worry that something exactly the same exists already, somewhere in the literature, and you just haven’t found it.

When I first heard the premise of the show, I thought that there would be at least one clued up scientist debunking the myriad of mediums, pentecostal ministers and downright crazy people making up stories about receiving personal visitations from their closely departed friends with tales of how pleasant the afterlife is. Alas, I was sadly mistaken. In fact, the only guy on the program who had any scientific background made the whole thing WORSE by completely fumbling his description of what is currently understood in the field of neuroscience and reducing it to ‘we really don’t understand at all how the brain works’ – wrong – ‘but we understand how vision works, for example, and if you remove a person’s eyes they cannot see, therefore how can they claim to have seen things after death’? – this was a.) irrelevant and b.) incorrect. The brain can simulate the experience of vision through memory prediction, (think about for example when you ‘see’ in a dream). It can also link other sensory inputs with predicted visual input (e.g. identifying objects whilst wearing a blindfold allows you to ‘imagine’ how the object looks).

Having just read Jeff Hawkins’ On Intelligence (thank you Geordie for recommending that one), the counter-arguments to a rational, physical theory of intelligence seem all the more strange. This book puts forward a beautiful computational model of the brain, which explains how intelligence can arise, why humans are so much better at certain tasks than computers, and also explains the hiaracy of intelligence in animals in terms of this model.

Indeed, after reading this book, I have now become even more of a Strong AI supporter.

How can people not acknowledge that the brain could be explained by a beautiful theory, instead wishing to attribute some ‘mystical’ connotations to it? Why does someone not having the wish to understand how something works give them the right to argue that no-one will ever have the capability of doing so? Why do their mystic arguments hold any water at all?

Because it’s easier to resign yourself to some fuzzy, warm, but completely incorrect way of thinking than to actually work damn hard towards the real understanding. And this laziness perpetuates itself.

Of course, people can resign themselves to this if they so wish, but they should not be allowed to preach it on live television as though it’s a scientifically sound point of view: Just some equally viable ‘alternative’ to the best possible scientific enquiry with which people have been diligently pushing forward the boundary of understanding for centuries.

What a horrible, deepening blow to the scientific community to let the people who know the least appear as experts via this incredibly popular and trusted style of information dissemination. And it’s downright wrong to give the impression that the argument is stronger from the mystical side and that the science itself is weak, just because you invite more passionate and ‘knowledgeable’ *cringe* mystics than scientists themselves.

This all makes me slightly annoyed, but all the more determined to actually a.) understand the cutting edge research in this field so I can b.) disseminate it correctly to these people.

To top off the woo-week, I watched What the Bleep Do We Know!? – Basically because I’d heard from the physics blogosphere that it was really, really awful. And hail! The physics blogosphere was correct I would still highly recommend the film to anyone who works in QM or even Physics/Biology in general, because we should be aware that stuff like this is not only in circulation, but is actually believed to be factual by many people. Maybe we should make a sequel: What the Bleep Do We Know!?: Quite a lot actually, if you ask reputable scientific people… Subtitle: - And don’t edit their responses to favour your own warped point of view –

I also felt that the copy of the film was better off in the relatively safe quarantine of my woo-antivirus vault (aka DVD shelf) than publically accessible (I found it in HMV). I did however feel slightly wrong after actually paying for a copy :S

One thing I did actually quite like about the film were the Dr. Quantum animations, I think they were very visually appealing. They would be a great way to enhance the teaching of (correct) science to kids, and indeed to interested people of all ages.

OK, end of PZMyers-esque-ness

In this picture we are testing that the apparatus cools down OK. This type of fridge is an Oxford Instruments Heliox, which is a He-3 based cooling system. The principle of operation is that you cool a ‘plate’ of metal to 1.5K by pumping a small amount of liquid from the main He-4 bath. The low pressure causes the He-4 to rest at this lower temperature instead of 4.2K. The cold plate is then brought into contact with He-3 gas, which condenses and drips down into a ‘pot’. Once you have condensed all the gas, you then pump on the He-3 pot itself (in the same way that you did with the He-4), which causes the liquid to slowly evaporate and cool as its tries to equilibrate its vapour pressure with the partial vacuum you create. However, unlike He-4, He-3 cools to much lower than 1.5K. In fact the temperature can be maintained at ~250mK for up to 90 hours, and you can supply extra heat to get a stable temperature anywhere up to around 1K.

This apparatus will be used to measure further interesting properties of unconventional pi-Josephson junctions at low temperatures. These junctions will (hopefully) eventually be of use in quantum computing applications: A built in phase shift across the junction gives rise to a degenerate ground state at zero applied flux, removing the need for application of a flux bias to obtain the qubit working point (a superposition of macroscopic quantum states).

With this one going too, it means I’ll be running 2 experiments (MQT on the dilution fridge and conductance on the CF), helping run this one (300mK heliox system), and building another (second dil fridge wiring for co-planar resonator coupled to qubit stuff) all at the same time. Argh! Still, lots of fun to be had.

For additional food for thought here is a delightful cake. A chocolate orange cake in fact. I made this one at the weekend. Excuse the quality of the photos, I only had a phone-cam at the time.

The book concentrated on how nanosystems will be used to transform our lives, our bodies, and the environment. There was also a discussion on how we will control nanosystems such that they do not replicate uncontrollably. The book was a nice introduction to the topic, not too heavy, and written with a powerfully optimistic style. I felt that the chapters on government policy were the weakest point, although to be honest that’s probably just my personal taste. They were well written, just not quite as gripping as the discussion of the actual technology itself.

The entire book was great reading, although I felt Chapter 14 in particular had something important to say; a lesson to be learnt. The focus of this section “A network of knowledge” was on the then future technology of hypertext, linked media and general freedom of information/knowledge aggregation techniques. It really stood out for me, because it’s the only chapter in the book where that technology today has not only been realised, but has exceeded Drexler’s foresight tenfold. Reading this chapter was so beautifully quaint, until a thought struck me…

…It could have been any of the chapters that had been fully realised.

Presumably all the chapters were written with a similar level of foresight, it just happened that the correct set of factors converged to cause 14 to be the first. I’m sure in due course more will follow, but this chapter sat somewhat uncomfortably and laughably in stark contrast to the rest of the (seemingly visionary) book. It humbly served to highlight our different behaviour towards incredible concepts that have already been realised, and those that still harbour engineering problems to be solved. Some would refer to the latter as (rather derogatorily) pure science fiction.

I also really enjoyed the last chapter too. Drexler’s writing style seriously moved me. I hope that people find this book a call to arms, and that those who read it when it was first published (1986) will take the time to re-read, and realise that we are closer to these dreams, enough so to really do something about it. In 10 years time I want to feel that quaint warmth when I read ALL the chapters, not just the one about hypertext.

I have the nanosystems textbook waiting on my bookshelf for some more in-depth learning.

I was rather enamoured by various artsey and atmospheric pictures available on the internet of people’s wirebonding endeavours, so I thought I’d try and capture a few of my own:

The process is as follows: First the chip (the dark coloured rectangle) is glued down onto a chip carrier (the large light coloured surrounding piece). The carrier may vary in design depending upon how many contacts are needed to be made and what apparatus that the chip is to be mounted within. Specifically, for very low temperature experiments it is important to have the chip in good themal contact with a metallic chip carrier (preferably Copper). A more specialised chip carrier for use in a dilution refrigerator is shown below:

The chip carrier is held firmly in the wirebonder vice, and then the wirebonder tip (which is like a needle with the metallic wire threaded through) is brought down into contact with the surface of the bonding pads on the carrier. The tip undergoes ultrasonic agitation, and is sometimes heated, which mimics a welding of the wire to the the metallic tracks below. As the tip is then moved by the user the wire is pulled through the needle and brought down again to form the second bond to the chip, at which point the bonder also cuts the remaining end of the wire. You end up with a small, neat loop of wire between the chip and your chip carrier.

Most of the time Sometimes, the wire comes unthreaded from the needle-tip.
It’s awkward to rethread, the hole through which you pass the thread is around the back of the needle, inclined at a 45 degree angle, and virtually invisible. It is necessary to use extremely fine tweezers to grab hold of the tiny thread. Here is a picture showing the rethreading process:

Demonstration of two-qubit algorithms with a superconducting quantum processor

Is being reported in several places at the moment, see here and here for example.

Whilst I’m confident that it works well in the few qubit limit, I’m not at all convinced that cavity QED is a scalable QC architecture. We’ve done some work on this architecture ourselves. There’s only so many qubits you can fit into one of these microwave resonators, and that’s without thinking about bias lines, error correction etc… I’d like to see a proposal for a scalable version.

We typically show some demonstrations with Liquid Nitrogen, verify that gases do not follow the ideal gas law using balloons, show electrical resistance at low temperatures, explore Lenz’s law at different temperatures using magnets and copper tubes and show some of the lovely properties of (High-Tc) superconductors such as the Meissner effect by levitating magnets.

The talk also introduced some fairly advanced concepts such as some fundamental aspects of Quantum Mechanics and practical Quantum Computing. In fact we have a HTC SQUID set up in the talk demonstrating the wave properties of electrons, analogous to the Young’s double slit experiment. The idea is to whet the appetite of potential students and make them realise that Physics isn’t all astronomy and the LHC, and that device physics/QC can also be a damn sexy thing to study

It’s all rather fun. I had this great toy to play with called a ‘visualizer’ which is basically a camera hooked up to the projector. Great for showing demos. I even zoomed in on some SQUID chips to show the audience the results of the microfabrication process.

Eventually I’m hoping to get a video of the talk up online when I’ve harrassed someone into filming the event and done it enough times to get all the demos working smoothly. We had issues with a dodgy multimeter this time. Lesson of the day: Never work with children or electronics.

Although Euroflux is based on a European framework, the conference is international, and people are welcome to attend. You can read my review of EUROFLUX2008 here.

The abstract deadline is Monday!! See you there

Picture © General Fusion

Speaking of cleaner energy, this story was all over the main news channel yesterday, discussing climate change in the UK in 60-70 years time. What? Why do people insist on extrapolating current trends without even considering technological advancements (exponential ones at that)? I can’t believe that people have such a pessimistic doom and gloom attitude towards this issue. They should be running news stories about research and progress towards future methods of active, local and global climate control and advances in green technologies. I seriously believe that by 2080 we’ll be thinking it was rather quaint that people were so worried about the fate of the planet from the point of view of traditional, naturally occurring weather systems. And our ability to produce large amounts of CO₂ detrimental to our own survival will be more likely seen as a bygone era once we are able to control the global climatic conditions.

I do still believe we should make what changes we can now to reduce further compunding of the energy crisis. Hence why I’m saving up for a hybrid car. But think about the probability that someone, somewhere will engineer and commercialize (Note: not even invent – there are plenty of good ideas already) a green energy technology which can be implemented globally within the next 50 years. I don’t think that seems unlikely. I get really irritated with the large proportion of technologically traditionalist and Luddite views in this country, especially when they dominate news headlines!

However, this is slightly irritating:

“This means computers would become exponentially more powerful because each “quantum bit” (qubit) could store a much greater range of numbers than the two that binary math restricts us to. Imagine a laptop with the computing power of the world’s 10 most powerful supercomputers. Then you begin to grasp the potential of quantum computing.”

In the spirit of a very popular television program:

Let me explain for any readers who are slightly confused at this point: Quantum computers will be very good at solving certain types of hard problems somewhat faster than classical computers. This should become some sort of mantra. (If anyone can think of a catchy version that would be cool).

They won’t be general purpose machines. The best way to think of a QC is more like a co-processor (say like a hardware graphics accelerator).

The types of problems that they will be good at solving are exciting and interesting in themselves. Quantum computers are cool enough without the overhype

Photo © Oxford Ion trap group

Alas, it is still only possible to manipulate a couple of qubits at the moment, as is the case with most QC realizations. The qubits themselves are formed by manipulating transitions between energy levels of the ions. In this case, Ca+ ions. Entanglement can occur for example between the ion and the photon emitted during a relaxation from an excited state. Therefore one advantage of Ion trap QC is that it is natively good at handling static (ionic) and flying (photonic) qubits with the same technology, and quantum information can therefore be transferred over long distances and on/off chip quite easily.

Exciting stuff. I’ve still got my money on Josephson junctions, but competition in experimental QC is healthy

Links:

Overview of the Oxford Ion trap
Nature paper on Ion trap QC
Info from MIT
PhysOrg report

Classically, the phase gets excited out of the metastable minimum due to thermal fluctuations (the state gets a ‘kick’ out of the well from the thermal energy available in the system). As the temperature is decreased, this is less likely to happen, and so the state stays in the well for longer, and the histogram gets narrower. If the junction is small enough it may be possible to see escape due to quantum tunneling of the phase, a competing escape mechanism. This is a temperature independent process and the width should saturate at low temperatures if quantum tunneling occurs. Unfortunately, the prescence of external interference gives a very similar effect. So here we measure a large junction, which should behave classically to a very low temperature. Any saturation of the width would demonstrate noise limitation.

This is a textbook response: The straight line demonstrates that the system follows the thermal activation theory, and furthermore is not noise limited. So in future measurements of junctions, any saturation observed must be either due to quantum effects, or noise sources intrinsic to the junction itself.

So I can now believe that I’m seeing real quantum processes in the junctions.

This makes me happy.

The dewar containing the liquid Helium can be seen on the left. The setup is rather simple, you have a gas pump, a thin transfer tube (the silver tube joining the dewar to the cryostat) and the sample space (inside the brass-coloured cryostat body). When the pump is turned on, it draws helium from the dewar through the thin tube and the sample space and returns it to a pipe on the wall (part of the overall in-house helium gas system installed in all the labs). Because the transfer tube is thin, the liquid Helium comes through quite slowly (you can adjust the flow rate) and a single dewar of liquid Helium (which holds ~45L) can therefore go a long way.

The electronics to control the experiment can be seen on the rack of equipment to the right in the photograph. This particular setup allows current-voltage (IV) characteristics and differential conductance (dI/dV) measurements to be performed on Josephson Junctions, with a PC controlled DAQ (data acquisition) system. A patch panel on top of the experimental insert allows up to 20 DC lines to be chosen for the measurement, so that many junctions to be tested in the same run.

Controlling the temperature on this type of cryostat is notoriously tricky. The generally accepted method is to run the pump to cool the system, whilst simultaneously heating the sample until the temperature stabilises at the required value. Which is a bit wasteful, it’s like running the heater and the air-con in your car at the same time. It can be done automatically with a PID temperature controller. Another way to obtain a measurement as a function of temperature is just to record data whilst the system cools down to 4.2K whilst the pump is running, or warms back up to 300K after the pump is switched off. Doing this on the warming cycle is slightly more stable. Additionally you don’t have the additional electrical noise of the pump if you are trying to conduct a low-noise experiment. This is a good way of obtaining Resistance-versus-Temperature (RT) measurements, which allow you to see the point at which your sample/junction goes superconducting. From this data you can also obtain the Residual Resistivity Ratio (RRR) which gives a measure of the quality/purity of the material being used.

A couple of thermometers are usually placed near the sample to give a good idea of the temperature gradient inside the cryostat. If there is a high temperature gradient near the sample, the temperature you read on your thermometer (which is generally a few cm away) might not be the exact sample temperature.

One of the best features of the CF is that if you run the pump for long enough, once the temperature inside the cryostat gets down to 4.2K, liquid Helium starts to collect at the bottom. Once you have collected some, you can turn off the pump and the system will stay at 4.2K until the collected liquid has all boiled off. This is a nice way to ensure that your measurement stays at a definite temperature for about an hour. Of course we don’t turn the heater on when we have liquid Helium in the bottom of the cryostat, or else the top of your experiment blows out and hits the ceiling as the Helium gas tries to occupy 700 times its liquified volume
With Niobium based Josephson junctions (Tc~9K), 4.2K is a good temperature to take measurements of their superconducting IV curves.

Why would you want to measure IV curves? Well, that’s another post

However there are some descriptions which aren’t so far fetched, such as the conference venue being a picturesque tropical coral-reef island. That one does happen occasionally.

I worry that this kind of thing will saturate very quickly due to not enough content added in enough areas for the idea to reach critical mass. However as a counterargument, look at the popularity of arXiv. I think there’s a balance between reporting scientific progress because you enjoy doing so (and you want to increase the SNR), and because you get something out of it (publications/more research grants etc.). ArXiv is very much a case of ‘I was writing the paper anyway’. Perhaps scholarpedia may become popular amongst those who would like to write academic books about specific topics, but aren’t quite fully fledged.

No-one can argue that at the moment the site needs more content. I was wondering if anyone has considered (or been earmarked for) creating a section on quantum computing. Perhaps the information already available on Quantiki could somehow be reproduced or linked.

However for a post-singularity human-machine hybrid with bio-electronic enhancement and connectivity to all other such instances, it would easily be possible. This state may well occur in the near future.* However, in order to bring about this revolution as quickly as possible, one must break a vicious circle and embrace the Polymath philosophy now. In order to bring about a singularity, we need to start working towards weaving together many fundamental technological and scientific strands of knowledge and understanding.

So I come to my point, which is that people in specialist subjects really need to read and actively work outside of their speciality. This breadth-focused approach may seem detrimental in some ways to the depth-based learning pursued by most academics. But hey, we’re really shooting ourselves in the foot here.

Breadth-based learning can complement an academic discipline. Concepts from far reaching areas can give ideas, insight and inspiration. They can even provide a motivation and reason for studying one particular area in such detail. For example studying the materials science of Josepshon junctions may help towards the implementation of quantum computers, leading to applications in many diverse fields. So you should learn as much as possible about those diverse fields.** [Note this may not be an entirely hypothetical example ] Over the past few months I’ve been talking to quite a lot of people in different disciplines and concluded that we should really be talking and interacting more. Not only does it spark interesting and fun conversation, there can actually be tangible outcomes from this kind of collaboration.

This far-sight can be used as such a powerful motivational tool for research progress that I’m surprised traditional narrow discipline based teaching methods still exist. As such I feel that projects such as Singularity University are groundbreaking, and will pave the way forward.

Part of the Polymath philosophy would presumably involve teaching at a high-level from a young age. Current disciplines of science, engineering, mathematics etc. may well move towards a more connected scheme of understanding. Perhaps we can accelerate progress towards the Polymath state by embracing such learning models now. It’s a delicate problem of striking a balance between the depth and breadth of education.

* I may have been reading Kurzweil recently…
**As an aside this thinking also helps you to write successful grant applications!

I went along to see if anyone was interested in Quantum Computing
Specifically I wanted to meet people who might want to explore QC (in the future) as a way of simulating neural nets and addressing some NP-HARD problems found in biological systems. And I met a few cool people, so that’s all good; it may be possible to establish some new collaborative links for future projects.

And being a little worried that people might get the wrong end of the stick and presume I was trying to out-parallelise the guy in charge of the HPCC, I explained that it would be a very useful resource for running QMC simulations…

FINANCING SCIENCE – Needed: Professional Research Technicians: Government Funding Is Up, But Professionalism Still Suffers
(via Science Progress)

Often grants cover salaries for postdoc time, PhD student time and tenured academic staff hours. But what we need more often than not (especially in the experimental scenario) are skilled technicians, not just workshop technicians but people who actually know how to operate, modify and improve equipment, and understand on a basic level the experiments being performed. They don’t necessarily need an academic calling to perform such a job, but they would need a financial incentive as it would be rather specialised.

At large facilities these guys are known as ‘Instrument responsibles’.

Alternately, perhaps full economic costing in research proposals should include a slice for consultancy (e.g. for low temperature physics projects it would be very useful to have some DAQ / interface electronic engineers). Any offers welcome but I’m afraid I can only pay you in pizza at the moment

The talk was about “Cryonics in the UK: Reality and Vision”. I never really realised before that there is so much of a UK effort in this area. It was also rather relevant given that I’m an advocate of H+ practising low temperature physics. I went along as a skeptic, (especially as I usually try to AVOID coming into contact with cryogens ) but there were some extremely convincing arguments presented and the speaker responded well to the audience questions.

The main aspects of the talk were as follows:

Definition and introduction
Cryonics is the preservation of tissue after death using extremely low temperatures. Cells and DNA are preserved and do not break down as they would do if kept at room temperatures after death. The main idea is to warm and revive patients at a suitable time in the future such that nanotechnology will be advanced enough to repair any damage incurred during the freezing process. Patients pay for the privilege of being cryopreserved in this way.

Are we there yet?
Around 200 patients have been cryopreserved so far. No-one has of yet been revived, although tests on embryos have been successful with a survival rate of 80% of cells. Also, rabbit kidneys have undergone a complete vitrification process and have been transplanted successfully.

Financial matters
To be cryopreserved costs between $30,000 and $150,000 depending on the company and if you would like just neural preservation or whole-body preservation. It should be noted that a good life insurance policy will more than cover this cost.

Objections to cryonics
The main objections were from moral and religious grounds: “Death is the natural way” arguments. There was also the continuity of consciousness problem, i.e. will you be the same person when you are awakened if so many of your cells have been changed. The counter argument is that the body is essentially a meat machine and if all cells (and inter-neural connections) are reconstructed to their original state, the brain will effectively be unaltered by the procedure. Hard-wired memories and psychological traits will remain.
There are also objections about awakening in a strange world, but these are personal considerations. The general consensus is, if you prefer the idea of death over being in a strange world, that’s your choice. (I personally think it would be interesting to wake up in the future).

Practical considerations and problems
How will reanimated people integrate into society? How will they make money? Perhaps by this time we will have transcended the need for money. There may be memory loss / brain damage if the process cannot be fully reversed. In addition, terminal conditions prior to death will also need to be remedied. Cancer/cell damage may be easier to fix than viral/DNA damage once the patient is revived. However, both of these problems will presumably be overcome as technology advances further.

Even though the technology and science of cryopreservation needs to mature substantially, the main problem currently lies with political, geographical and social constraints. Cryonics is not legal in several countries/states. The only operational facilities are currently in the USA and Russia. In the UK, Cryonics UK offers a standby team who will ‘look after’ your body upon death and perform as much of the procedure as possible until you can be transported to a full cryonics facility.

There is also the problem of many deaths requiring autopsy, which kind of screws up the whole cryopreservation process; being cut into little bits and all that. The body may not be released from the morgue for several days, which doesn’t help keep you in good shape for the process.
People can now request autopsy by MRI on religious grounds, so perhaps this is something to lobby about if you are signed up for cryo. And of course there’s the question: Is it necessary to know how you died if you believe you will be revived in the future? Maybe it is even more important to know how you died so that you can be fixed? I wonder if MRI autopsy could be performed on patients once they are in cryostasis…

The current procedure
The standby team/funeral director will inject Glycerol and Heparin to prevent blood clotting. The body is stored in an ice bath to slow cell degeneration. The cardiovascular system is then used to administer a cryoprotectant to all tissue and cells. This works a bit like antifreeze; lowering the freezing point of the water in the cells and preventing the damaging ice crystals from forming so readily. If enough cryoprotectant can be administered, a large fraction of the body’s water does not freeze at all. However the full extent of damage to cells by the toxicity of concentrated cryoprotectant itself is not yet known, so a balance is struck with the amount administered. The body is then cooled using LN2 to a temperature of 77K (-196 degrees C) and popped in its new home, a cosy cryostat, for storage.

Here are some great links to learn more:
Cryonics UK
Cryonics Institute
Alcor
UKTA

For further information, Alcor has an excellent FAQ on their website which explains everything much better than I have attempted to here!

Warning: personal opinion ahead…

So what’s the deal? Should I sign up? Cryonics is sometimes regarded as the dark side of H+. Well, I guess we can think of this as a plan B. If we aren’t able to acheive full topological & functional mapping and digital reconstruction of specific instances of the human brain (aka uploading) in the next 50-60 years, it might be a good idea to have a backup plan…

Speaking of which, after the event I met and talked to Anders Sandberg about brain modelling and quantum computing, which was extremely useful and very cool.

TYPEALYSER

Typealyzer parses the content of your blog and gives you a Myers-Briggs style result as to the ‘personality’ of your blog. P&C comes out as INTJ scientist type. What jolly good fun.

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Think you know topology? Try to get your head around this:

How to turn a sphere inside out
(via The truth makes me fret)

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Because ‘Women in STEM’ sounds so last century….

The nerd girls and their amusing website:

At the bottom you can just see the top of the outer mu-metal shield. It’s actually quite a lot longer than this, I’d already lowered it a fair way into the dewar. Inside this shield is a second, inner one, and inside that is the superconducting magnet (solenoid). Inside the bore of the solenoid is the IVC, inside which is the sample box, and inside that the Josephson junction is mounted. Which makes it sounds a bit like one of those childrens’ pop-up books or interactive posters where you ‘look under’ parts (I should really make one of those, it would be cool to show to visitors).

I’m thinking of adding a third mu-metal shield. I do have one that will fit, but it will involve some ‘fridge modification’ (aka a hacksaw to the Lambda coil) so I think I’ll see how it does with 2 layers for now.

Addition of the magnet allows the critical current of the Josephson junctions to be changed in persistent mode (with less flux noise from the magnet itself) and the shields should help attenuate external magnetic interference.

So we’re ready to rock!

How to Map Neural Circuits With an Electron Microscope

“This giant, and potentially revolutionary, task requires custom software, electron microscopes and an incredibly sharp knife. If everything goes right, the team may be the first to create a circuit diagram that explains how mammals see.”

Photo Credit: Marc Lab / Moran Eye Institute

This is just too cool for words.

I’m reading permutation city by Greg Egan at the moment – which paints a picture of a world where the human nervous system has been sufficiently ‘mapped’ to such an extent that simulations of the brain can be run resulting in artificial (or actual – you decide) intelligence. Strong AI for the win! *ahem*

How far away is this technology? If we can map neural pathways by taking slices, scanning them with an SEM/TEM, and and automatically reconstructing the 3D original using clever software.. then all you need to do is bring it to life digitally using a wetware-friendly circuit simulator – I’m not sure such a thing exists… If not, lets develop one and call it MeatSPICE

Seriously, I have a colleague who does work on electrical models of the nervous system. You can essentially model neurons and axons as electrochemical transmission lines with inductance, capacitance and resistance. It’s a good way for physicists, biologists and biochemists to collaborate.

I also have a slight penchant for this kind of thing seeing as I love using the SEM as an imaging tool. In addition, I recall spending a couple of weeks once doing some work experience at the histopathology department of my local hospital; watching how you actually make those waxy slices and observing them under an optical microscope. (Although we were looking for cancer cells in that particular case and musing over how you could automate such a task).

I’d love to put a load of these ideas together into something tangible to research (as an aside to my already busy schedule!), but it’s a wide, diverse assembly of proto-thoughts (of which I have far too many), requiring quite a large collaboration from different fields.

On a slightly different note, WIRED has now been released as a magazine version in the UK. Which means all my pocket money is belong to them

These are bona-fide lab white boards with real physics-working-stuff notes… Look, there’s even equipment lying around nearby!

I’d highly recommend watching this film. I found it really moving. Here is the Wikipedia entry for the film, including quite a few opinions from film critics. Trying to normalise away any potential political spin here, Al Gore seems like a nice guy who actually cares. And indeed one who knows his stuff.

A few film critics accuse the movie of skewing the data, however I’ve attended several seminars on climate change recently, and the results reported in those seem to agree with what was presented in the film. They all seem to paint a similar picture.

‘Nine errors’ have been pointed out in the film, points used for impact which are unsupported by current scientific studies. This news report gives a good summary. The film is allowed to be shown in schools as long as ‘guidelines’ documenting and explaining these errors are included.

The film gives some useful information and tips at the end, and believe me even if you *think* you’re being green, you could probably improve on it tenfold. The film has seriously made me consider buying a hybrid car.

On a slightly unrelated note, the film also demonstrates how to essentially make a movie out of a seminar consisting of a slide-show presentation. It works really well and keeps the audience attentive. I think this is generally a good way of presenting physics/scientific ideas.

“Over two-hundred education & science blogs”

A nice report mainly consisting of a compilation of educational blogs, with notes about advantages and problems arising from the use of the growing blogosphere as a tool for the dissemination of scientific knowledge. Each listed blog has a blurb, a weblink, and a Technorati and Blogger rating (where available).

This is a really cool and novel use of superconductors:

Reconfigurable Spacecraft as Kinematic Mechanisms Based on Flux-Pinning Interactions

Mu-metal shields help screen the experiment from magnetic fields, both varying (such as from local sources of interference) and static (e.g. the earth’s field). Nesting them increases their effectiveness. Mu-metal workings by ‘sucking’ in field lines – they prefer to go through the high permeability material than through air (or free space), so they are diverted around a sensitive sample in the middle.

Magnetic field affects the Josephson Junction by altering its critical current, I_c. The experiment I am performing involves precise measurement of I_c. Ironically, in the middle of all these shields a magnet will be fitted (in the picture this is the smallest item at the right hand side of the line-up). The magnet allows I_c to be changed in a controlled manner when necessary.

The magnet is superconducting so that it can be operated in persistent current mode. This is where the current supply is isolated from the magnet’s superconducting coil using a special switch and the current flowing in the coil then just carries on going round forever. This is good news as it means the field is extremely stable.

At the moment the experiment only has 1 magnetic shield, which is not shown in the picture. Now that I have these new shields, I will nest 3 of them (note I can’t nest them all due to both geometry and space reasons) to improve the experiment. I will assemble the shields inside the dewar (the big blue thing in the background) housing the liquid helium which cools the Josephson junction experiments.

So far we’ve had talks about single photon detectors, hot electron bolometers, junction measurements, ballistic readout of qubits, Terahertz imaging, modelling inductances and superconducting circuits, cryocooling and cryopackaging, and plenty of RSFQ related information.

Yesterday morning we had an invited talk given by Hideo Suzuki from ISTEC, Japan, entitled ‘Progress of the interface circuit and cryopackaging techniques for SFQ circuits’. The Japanese progress on RSFQ technology is very impressive, the presented results demonstrated some very high speeds with very low Bit Error Rates (BER).

The weather is lovely and the food is delicious which always helps

Part of the idea of S-Pulse is to get people involved in the RSFQ/SCE scene, and to disseminate information about superconducting digital electronics and why it is useful. So if you are interested in attending or contributing to a workshop, you can check out the S-PULSE website for information. So far people are meeting and networking within this European project from Germany, France, UK, Sweden, South Africa, The Netherlands and Italy, to establish the position of Europe in the field of SCE and to promote the European superconducting device foundry. Note: People from outside Europe are also encouraged to attend.

Also, there is a postdoc position and a couple of PhD positions currently available in RSFQ. More information can be found here.

“Funding of science must be reviewed, say Tories”

The basic idea is that the Haldane principle needs reviewing. This is the idea that researchers and scientists themselves decide what research should be funded, as opposed to a higher (governmental) system. The Haldane principle seems like a sensible idea to me. The fact that it has been around since the start of the last century doesn’t necessarily make it outdated.

Whilst I agree that issues like climate change and green energy need to be addressed, allowing politicians to become more involved in decisions about research, especially fundamental research, cannot be a good thing. I believe (or perhaps naively like to think) that researchers (even those at the top of the funding hieracy) are on average more motivated by their curiosity towards scientific discovery, rather than what will make them the greatest profit or keep them in government. And besides, do politicians really want to spend their days discussing detailed scientific issues in which they are neither qualified nor interested?

As a final point, the government (or shadow cabinet in this case) should really stop riling scientists like this. We’re paid lousily for our contribution to society, and there’s precious little money invested in fundamental research as it is. At least let us keep the right to choose what we want to research.

Right – that’s my political rant over for the year

10.) Chiselling ice off the cold traps
You have to do this otherwise you can’t get them out. Some cold traps are better than others, the ones I have on my fridge aren’t very good, they get iced up quite often.

9.) Adding exchange gas to the IVC.
I use Hydrogen exchange gas, which allows the innards of the vacuum can to cool by conduction and convection, before the temperature gets low enough to freeze out the Hydrogen (at which point it becomes a pretty good vacuum). However, adding it to the IVC is slightly irritating as you need to connect up a vacuum pump, balloon of Hydrogen, etc.

8.) Calibrating thermometers
Thermometry is a pain. Alas, it is also one of the most important parts of the apparatus, as temperature is a rather important variable in most experiments. Writing software calibrations, entering long tables of values, using generic calibrations if you’ve lost the long tables of values….

7.) Making leak-tight He-4 to vacuum feedthroughs.
Stycast is your friend They have to be tested by being dipped into a dewar of helium, via a hollow feedthrough rod to which they are soldered in place at one end, whilst the other end is simultaneously connected to a leak detector.

6.) Winding magnets / making superconducting persistent mode magnets
Spot welding superconducting joints for heat switches and winding/potting/quench training magnets – argh!

5.) Making custom filters
Commercial kit just doesn’t work at low temperatures. I’ve had commercial filters fail a few times, and it’s not an easy bug to fix, especially when they only stop working at liquid helium temperatures…

4.) Soldering coaxial cables/adapters
Luckily I now almost exclusively use CuNi cables, which will accept standard solder. Prior to that, stainless steel was the only real option. Soldering to stainless steel is difficult.

3.) Leak testing apparatus
It isn’t fun but it really does need doing each time you remake an Indium seal (for example). The hassle a leak would give you once the apparatus is cold isn’t really worth the few hours it takes to do a leak test.

2.) Fixing leaks in the apparatus
Whilst leak testing is irritating, fixing a leak is even more so. Especially superleaks (leaks that only occur at Liquid He tempertaure). A world of pain.

1.) Filling the fridges
Yep, it’s the most boring job in the world. It takes hours and needs doing every couple of days depending on how hungry your fridge is feeling. Luckily, closed cycle systems are becoming ever more popular. Here’s to a bright, cryogen free future!

If people can think of any more I’d be happy to add them to the list…

Disclaimer: If this post makes no sense at all, don’t worry. I’m writing a series of posts on ‘Introduction to Josephson Junctions’ which might be useful to read first. However they’re a lot harder to write than posts like this, and I haven’t quite got them up to standard yet.

Here’s a schematic of the washboard potential (the energy landscape of a current-biased Josephson Junction) under the influence of microwave irradiation. As the current is increased, the potential becomes more tilted and the height of the barrier defining the metastable minimum decreases.

The phase is a macroscopic quantum variable, so the wavefunction of the system comprises of discrete eigenstates with individual energies (well, almost… you can’t solve the S.E. exactly for this case as the state is not fully confined, but there are ‘likely’ energy states in the well). The phase of the junction can escape from the potential via thermal activation or quantum tunnelling through the barrier, after which it ‘rolls’ down the potential landscape like a ball on a washboard – hence the analogy. This is detected experimentally as a sudden increase in voltage across the junction (a constantly changing phase across a Josephson Junction corresponds to the appearance of a DC voltage).

Here is a plot of data showing the rate at which the phase escapes from the well as a function of bias current:

The escape is linear as a function of current when plotted on this (somewhat manipulated) scale. Which it should be. The red line is a fit to the thermal activation theory at the temperature of the measurement, 0.065K.

The escape process can also be shown as a histogram, where each escape event is binned according to the current at which it happened. The plot below is for a different junction and temperature, but the shape of the histogram is the same. The fitted line again denotes the expected value from thermal activation theory.

However occasionally you see deviations from this result. Here the escape rate data have a different structure:

This can also be visualised as a histogram, as explained in the standard case. However, here we see that the histogram is doubly-peaked:

I have denoted this process an ‘enhancement’ as the background slope of the escape rate appears to be higher than the fit from thermal activation theory (shown in red). Physically this corresponds to the phase escaping from a higher energy level in the potential well, such that you do not need to tilt the washboard as much to obtain a high probability of an escape event. The histogram gets narrower and the escape rate gets steeper accordingly.

One interesting point is that the enhancement seems to provide exactly a 50-50 level population, which suggests some kind of saturated equilibrium process (as opposed to, say a population inversion).

Also, in this case, the ‘splitting’ – which can be seen from the histogram data, is about 11nA in 8.58uA, or about 0.12%, which is quite small and shows the kind of resolution you can achieve in these experiments. Unfortunately this is difficult to convert to an energy level spacing, as you need to know the ideal critical current I_c0 and exactly where the energy levels are in the well to calculate this.

So what causes the enhancement? It could be a source of interference at a particular frequency, for example the nearby wireless networks. It could also be a thermal enhancement, if the energy level spacing in the washboard is well below k_BT. But I’m not quite sure yet as to the exact origin of this behaviour.

Via WIRED:

DIY Freaks Flock to ‘Hacker Spaces’ Worldwide

A little more digging, and I find that there’s some already running in the UK, and one planned for Birmingham. Reminds me, I’ve been thinking about on organising an electronics masterclass/summer school. It would be mainly aimed at A-level students (the idea being to host it at the University as a means of attrcting students), but I guess that doesn’t necessarily have to be the case if you have a non-Uni based framework such as this in place.

As you might expect, there is more signal filtering before the DC lines enter the sample box. The filtering is again done by powder filters. These are housed in antechambers surrounding the main sample space. Here is a picture showing the coils which form the inductive component of these LC filters before they have been ‘potted’. You can also see the feedthrough capacitors entering the main sample chamber.

And here is one after showing the filters after they have been filled with stycast:

In the centre of the sample box is the sample space containing a chip carrier, the 4 filtered DC lines and 2 RF lines (which have not been connected yet here). It is important that the chip sits on a copper chip carrier which is screwed down tightly to the sample box. This is due to the physics of conductivity at low temperatures. If the chip carrier is glued down, there would not be very much thermal connection between the sample box and the chip carrier, as glues become very good thermal insulators at low temperatures. The best way to make thermal contact at low temperatures is to have two clean metal surfaces in hard contact. The sample box itself is attached to the mixing chamber of the dilution refrigerator, the part that does the cooling, so this too must be screwed down tightly.

The connectors on the outside of the box are MCX-style jacks for the DC lines and SMA jacks for the RF lines.

There is also a tapped hole on the back of the sample box where a calibrated Ruthenium Oxide thermometer is mounted.

This is amazing: A 3D virtual tour of the LHC. Complete with soundtrack of people making busy working noises

It’s somewhat cool to spend a lot of time in information-overload mode; it makes relaxing seem easier (even in a hectic city-lifestyle).

To further my explorations, I performed a tentative investigation of Twitter. Hmmm, I really should work harder at this early adopter thing :S So I got thinking about Twitter and things you could do with it…

There is so much information available. All that information can be used… as metrics for ‘things’. For example lets think about, say, ‘Starbucks’. If twitter feed information links Starbucks with ‘good’ or ‘amazing’ or ‘:)’ then the ‘worth’ of the item can be used as a metric of public opinion based on association. You could do the same thing will politicians, banks, countries, decisions etc. A bit like an opinion-stock-market.

Also, why not try direct comparison metrics – See how many times ‘Google’ is mentioned, compared to ‘Microsoft’.

It’s also a lovely dataset for training an AI by extract information from feeds, to learn more about human behaviour and reactions.

You could use it as a metric of generally literacy / intelligence, or as a way of tracking the evolution of language. Tracking internet memes would also be fairly easy, or tracking society moods based on location (if you could also log IPs for instance). Like this recent study on well-being in European countries.

You could do all this with blogs too, but I’m guessing that the blogosphere would give a rather biased subset of the general population in several ways. In addition, blog posts tend to be longer and as such may be more difficult to mine data or trends.

It would potentially be difficult to extract absolute conclusions from this type of study, but monitoring time dependence (i.e. performing some form of normalisation) would be very interesting. If anyone knows of any studies such as this one, it would be interesting to read them.