‘Computronium’ is really ‘Unobtainium’

‘Computronium’ is really ‘Unobtainium’

S. Gildert January 2011

Computronium [1] is defined by some as a substance which approaches the theoretical limit of computational power that we can achieve through engineering of the matter around us. It would mean that every atom of a piece of matter would be put to useful work doing computation. Such a system would reside at the ultimate limits of efficiency, and the smallest amount of energy possible would be wasted through the generation of heat. Computronium crops up in science fiction a lot, usually as something that advanced civilizations have created, occasionally causing conflicts due to intensive harvesting of matter from their galaxy to further their processing power. The idea is also also linked with advanced machine intelligence: A block of matter which does nothing other than compute could presumably would be incredibly sought after by any artificial intelligence looking to get the most compact and powerful brain for its money!

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

Pavlov’s AI – What did it mean?

So recently I gave a talk at the H+ Summit in Los Angeles. However, I got the impression that the talk, which was about the fundamentals of Artificial General Intelligence (something I decided to call ‘foundations of AGI’) was not fully understood. I apologize to anyone in the audience who didn’t quite ‘get’ it, as the blame must fall upon the speaker in such instances. Although, in my defense, I had only 15 minutes to describe a series of arguments and philosophical threads that I had been musing over for a good few months 🙂

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

The ‘observer with a hammer’ effect

Here is another short essay about quantum mechanics-related stuff. It’s a very high level essay, so any practising quantum physicists probably shouldn’t read it 😉 It is more aimed at a general audience (and news reporters!) and talks about the ‘spooky’ and ‘weird’ properties of superposition and decoherence that people seem to like to tie in with consciousness, cats, and ‘the observer effect’. It doesn’t really go into entanglement directly, I think that should be an issue for a separate post! It is also a fun introduction to some issues when trying to perform experimental quantum computing and quantum physics in general.

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.

Online seminar on Quantum Computing

I’m giving a VIRTUAL seminar in Teleplace this Saturday…

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!

Experimental investigation of an eight-qubit unit cell in a superconducting optimization processor

Anyone who follows this blog and wants to get a real in-depth insight into the way that D-Wave’s processors are built, and how they solve problems, should definitely read this paper:

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

Nice preprint from the Harvard group introducing quantum computing for chemical simulation, including a great deal about AQC and how to apply it to such systems, e.g. lattice protein folding and small molecules. Includes references to some experimental and simulation work done at D-Wave (write-up for that in progress).

Simulating Chemistry using Quantum Computers

How do you measure the speed of an Adiabatic Quantum Computer?

If you would like to know the answer to this question, head over to
rose.blog, where I wrote a guest post on this very subject!

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

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

An adiabatic tragedy of advocates and sceptics

I think the recent musings around the blogosphere and beyond are completely missing a huge and fundamental point about why building AQC and AQO-based machines will be not only useful, but something that we will wonder how we lived without. I’m not going to talk about the specifics of implementing superconducting technology for cool applications (I can defer that to subsequent posts). Here I just want to explain a little about why we should do it, and how the main hurdle to progressing such technology is nothing to do with our ability to build and understand the technology itself. In fact, it is a rather sad story.

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.

AQC / AQO video talk

Here is a video lecture that I gave a while ago about Adiabatic Quantum Computing and Adiabatic Quantum Optimization (specifically describing some cool things that you can do with D-Wave hardware) to my former colleagues at the University of Birmingham. This is a slightly higher level talk than the previous ones I have posted. Thanks again to my kind colleague and good friend (soon to be Dr.) Dominic Walliman for editing and posting these videos!

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 🙂

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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…

Beautiful AQUA@Home simulation results

There are some lovely simulation results coming out of D-Wave’s AQUA@Home project:

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!!