Cool Nanotech video

This is so awesome. Michio Kaku explains some of the latest developments in microtechnology for drug delivery, energy generation and military applications, and and how these current developments might be further miniaturized towards the nanoscale… I just love the little machine with the fat cell destroying laser!! *zzzt*

Via Blogging the Singularity

QIP 2010 – Further thoughts and CAKE!

So I’m back from QIP now, and full of chocolate. One might say I am maximally satisfied. However I didn’t have time to post this final update so I’ll do it now.

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.

QIP 2010 – Day 2, erm I mean 3

I’ve been so busy at this conference, I haven’t had much time to write stuff down. So yes, I totally suck at liveblogging 🙂

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….

QIP 2010 – Day 1

Hmmm. I think I AM the only experimental physicist here 🙂

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.

QIP2010 – Preconference musings

I’m now in Zurich at the QIP2010 conference. I’m hoping to do a bit of live-blogging! I’ve never live-blogged from a conference before, so we’ll see how this goes.

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!

Writing a cool lecture is hard. But rewarding!

I’m currently writing a lecture about…well I’m not quite sure what it is going to be about yet. It’s an IOP evening lecture, and I want it to be awesome.

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? 😀

Another amazing video

Another stunningly beautiful video visually describing our place in the universe.

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.

Quantum brains

I’m going to talk about quantum brains. But before I do, I have to take a bit of a philosophical detour. So bear with me and we’ll get onto the meaty quantum bits (qubits?) soon.

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*

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?