Make It Planar

Steve Flammia points me to this cool game. Well at least it’s cool if you are the computer science type.

Beyond Moore's Law

If Moore’s law continues at its current pace, sometime between 2040 and 2050 the basic elements of a computer will be atomic sized. And even if Moore’s law slows, we eventually hope that computers will be made of components which are atomic sized. Either way, we really believe that it might be possible to get to “the end of Moore’s curve.” A question I like to ask (especially to those employed in the computer industry) is “What will happen to your job when we hit the atomic size barrier for computer components?” (or more interestingly, “will you still have a job when Moore’s law ends?” Yes, I know, software is important, architecture is important, etc. I still think the end of Moore’s law will result in major changes in the industry of computers.)
One question which comes up when we think about the end of Moore’s law is that in some sense, the end of Moore’s law that we’re talking about is the end of a particular manner of creating fast computing devices. Mostly we are thinking about the end of silicon based integrated circuits, and even more broadly we are thinking about the end of transistor based computers (i.e. we include both silicon based circuits and also molecular transistors, etc.) And the fundamental speed of these devices is rooted in physics. So what can physics tell us about what lies beyond Moore’s law?
Well first of all, the length scales involved in the traditional version of Moore’s law are atomic length scales. The barrier we are hitting is basically the barrier set by the laws of atomic physics. But we know, of course, that there are smaller length scales possible. In particular the next step down the ladder of sizes is to go to nuclear length scales. But we also need to say something about the speeds of operations. What are the limits to the speeds of gates which an atomic physics based computer can operate at? Interestingly, there is often a lot of confusion about this question. For example, suppose you are trying to drive an atomic transition (this is nothing like our transistors, but bare with me.) with your good old tabletop laser. The speed at which you can drive this transition is related to the intensity of the laser beam. So it might seem, at first guess that you can just keep cranking up the intensity of the laser beam to get faster and faster transitions. But eventually this will fail. Why? Because as you turn up the intensity of the laser beam you also increase the probability that your system will make a transtion to a state you don’t want it to be in. This may be another energy level, or it may be that you blow the atom appart, or you blow the atom out of whatever is keeping it in place, etc. Now, generally the intensity of the laser beam at which this becomes important is related to the energy spacing in the atomic system (if, say you are trying not to excite to a different level.) Note that the actually energy spacing of the levels you are driving is NOT the revelant information, but most of the time, this spacing is the same order of magnitude of the spacings to levels you are trying to avoid. So this allows us to, roughly, argue that the maximum speed we will achieve for our transition is Plancks constant divided by the energy spacing.
Now for atomic systems, the energy levels we are talking about might be, say a few electron Volts. So we might expect that our upper limit of speeds from our gate is something like 10^(15) Hz. Note that today’s computers, which don’t operate by driving atomic transitions, but in a different manner, operate with clock speeds of 10^(9) Hz (yeah, yeah, clock speed is no guarantee of instructions per second, but I’m a physicist, so order of magnitude is my middle name.) Only 6 more orders of magnitude to go!
So what does happen if we hit the end of atomic sized computing devices? As I mentioned the next step on the length scale slash energy scale are nuclear systems. Here we find energy scales which are typically millions of electron Volts. But I have absolutely no idea how to build a computer where internal nuclear states are used to compute. Which, doesn’t mean that it’s impossible, of course (which reminds me of a great quote by the late great John Bell: “what is proved by impossibility proofs is lack of imagination.”) So there’s a good problem for a nuclear physicist with a few spare moments: think up a method for computing using nuclear transitions.
One can continue up the energy scale, of course. But now it gets even more far out to imagine how to get the device to compute. Is it possible to turn the large hadron collider, currently being built at CERN, into a computer opperating at 10^(27) Hz (energies of terra electron Volts)? Now THAT would be a fast computer!

A Fork In the Road for Ion Traps

Big news for ion trap quantum computers. It seems that Christopher Monroe’s ion trap group at the University of Michigan has suceeded in getting ions to shuttle around the corner of a T in their ion traps (their news item is dated 6/11/05, for this result.) This is, needless to say, a crucial step in building a “reasonable” architecture for quantum computing. This kind of thing makes me want to give up my theory license and jump into the lab!

Paper and Book Roundup

Some interesting papers.
First, a paper by Andrew Childs and Wim van Dam, “Quantum algorithm for a generalized hidden shift problem”, quant-ph/0507190 which gives a very nice, new algorithm for, well, for what it says: hidden shift problems! Interestingly their new algorithm uses Lenstra’s classical integer programing algorithm to implement an entangled measurement on the quantum states they set up. I just started reading the paper this morning. Once I parse it, I may have more to post.
Another interesting paper, is “Rigorous location of phase transitions in hard optimization problems” which is, amazingly, a computer science article published in…Nature. If you read this paper and are a physicist, it will make you very proud:

Our results prove that the heuristic predictions of statistical physics in this context are essentially correct.

In other words…yeah the physicists are actually really good at guessing what approximations to make! The paper is nice as well, rigorously proving some nice properties of random instances of certain NP-complete problems.
Finally, I received in the mail yesterday “Probability Theory” by E.T. Jaynes. This book, in incomplete form, had been available on the web for many years. Following Jaynes’ death, G. Larry Bretthorst was able to collect some (but not all) of this material into “Probability Theory.” Unfortunately, Jaynes’ had intended to have two volumes, and it seems that the second volume was woefuly incomplete and so will not be published.

Got Quantum Problems?

Scott Aaronson has written a nice article “Ten Semi-Grand Challenges for Quantum Computing Theory”. If you are a computer science theory researcher interested in what to work on in quantum computing, I highly recommend the list. One thing I find very interesting about theory work in computer science is how religious researchers are about not sharing the problems they are working on. So it is very nice of Scott to share what he thinks are big open problems in quantum computing theory today.
One thing that Scott leaves out of the list, which I would have included, are questions along the lines of “how can quantum computing theory contribute to classical computing theory.” Scott explicitly says he does not include this question because it is “completely inexhaustible,” and I agree that this is certainly true, but this may be exactly the reason one should work on it! The idea behind this line of research is to prove results in classical computational theory by insights gained from quantum computational theory. An analogy which may or may not be stretching things a bit is the relationship between real analysis and complex analysis. Anyone who has studied these two subject knows that real analysis is much more difficult than complex analysis. Physicists best know this in that they often cannot easily do certain real integrals unless they pretend their real variables are complex and integrate along a particularly well choosen countour. Similarly, many results in real analysis have counterparts in complex analysis which are easy to prove. So the line of research which asks whether quantum computation can contribute to classical computation is basically “as complex analysis is to real analysis, so quantum computing is to classical computer.” I’ve discussed this possibility (along with Scott’s contribution to it) here.

Best Title Ever? A New Nomination

On the quant-ph arXiv, today, we find, Steven van Enk having way too much fun:

Quantum Physics, abstract
quant-ph/0507189

From: Steven J. van Enk [view email]
Date: Tue, 19 Jul 2005 19:10:35 GMT   (2kb)

|0>|1>+|1>|0>

Authors:
S.J. van Enk
Comments: 1.1 page, unnormalized title

is entangled. (There is nothing more in the abstract, but quant-ph does not
accept 2-word abstracts, apparently.)

Full-text: PostScript, PDF, or Other formats

Erasing Landauer's principle,

Three Toed Sloth (who has been attending the complex systems summer school in China which I was supposed to attend before my life turned upside down and I ran off to Seattle) has an interesting post on Landauer’s principle. Landauer’s principle is roughly the principle that erasing information in thermodynamics disipates an amount of entropy equal to Bolztman’s constant times the number of bits erased. Cosma points to two papers, Orly Shenker’s “Logic and Entropy”, and John Norton’s “Eaters of the Lotus”, which both claim problems with Landaur’s principle. On the bus home I had a chance to read both of these papers, and at least get an idea of what the arguments are. Actually both articles point towards the same problem.
Here is a simplistic approach to Landaur’s principles. Suppose you have a bit which has two values of a macroscopic property which we call 0 and 1. Also suppose that there are other degrees of freedom for this bit (like, say, the pressure of whatever is physically representing the bit). Now make up a phase space with one axis representing the 0 and 1 variables and another axis representing these degrees of freedom. Actually lets fix this extenral degree of freedom to be the pressure, just to make notation easier. Imagine now the process which causes erasure. Such a process will take 0 to 0, say, and 1 to 0. Now look at this processs in phase space. Remember that phase space volumes must be conserved. Examine now two phase space volumes. One corresponds to the bit being 0 and some range of the pressure. The other corresponds to the bit being 1 and this same range of pressures. In the erasure procedure, we take 1 to 0, but now, because phase space volume must be preserved, we necesarily must change the values of the extra degree of freedom (the pressure), because we can’t map the 1 plus range of pressures region to the 0 plus the same range of pressures because this latter bit of phase space is already used up. What this necesitates is an increase of entropy, which at its smallest can be k ln 2.
From my quick reading of these articles, their issue is not so much with this argument, per se, but with the interpretation of this argument (by which I mean they do not challenge the logical consistency of Laundauer and other’s formulations of the principle, but challenge instead the interpretation of the problem these authors claim to be solving.) In both articles we find the authors particularly concerned with how to treat the macroscopic variables corresponding to the bits 0 and 1. In particular they argue that implicit in the above type argument is that we should not treat these macroscopic variables as thermodynamic-physical magnitudes. The author of the first paper makes this explicilty clear by replacing the phase space picture I’ve presented above by two pictures, one in which the bit of information is 0 and one in which the bit of information is 1 and stating things like “A memory cell that – possibly unknown to us – started out in macrostate 0 will never be in macrostate 1″ (emphasis the authors.) The authors of the second article make a similar point, in particular pointing out that “the collection of cells carrying random data is being treated illicitly as a canonical ensemble.”
What do I make of all this? Well I’m certainly no expert. But it seems to me that these arguments center upon some very deep and interesting problems in the interpretation of thermodynamics, and also, I would like to argue, upon the fact that thermodynamics is not complete (this may even be as heretical as my statement that thermodynamics is not universal, perhaps it is even more heretical!) What do I mean by this? Consider, for example, one of our classical examples of memory, the two or greater dimensional ferromagnetic Ising model. In such a model we have a bunch of spins on a lattice with interactions between nearest neighbors which have lower energy when the spins are aligned. In the classical thermodynamics of this system, above a certain critical temperature, in thermodynamic equibrium, the total magnetization of this system is zero. Below this temperature, however, something funny happens. Two thermodyanmic equilibrium states appear, one with the magnetization pointing mostly in one direction and one with the magnetization point mostly in another direction. These are the two states into which we “store” information. But, when you think about what is going on here, this bifurcation into two equibria, you might wonder about the “completeness” of thermodynamics. Thermodynamics does not tell us which of these states is occupied, nor even that, say each are occupied with equal probability. Thermodynamics does not give us the answer to a very interesting question, what probability distribution for the bit of stored information!
And it’s exactly this question to which the argument about Landauer’s principle resolves. Suppose you decide that for the quantities, such as the total magnetic field, you treat these as two totally separate settings with totally different phase spaces which cannot be accessed at the same time. Then you are lead to the objections to Landauer’s principle sketched in the two papers. But now suppose that you take the point of view that thermodynamics should be completed in some way such that it takes into account these two macroscopic variables as real thermodynamic physical variables. How to do this? The point, I think many physicist would make, it seems, is that no matter how you do this, once you’ve got them into the phase space, the argument presented above will procedure a Landauer’s principle type argument. Certainly one way to do this is to assert that we don’t know which of the states the system is in (0 or 1), so we should assign these each equal probability, but the point is that whatever probability assumption you make, you end up with a similar argument. in terms of phase space volume. Notice also that really to make these volumes, the macroscopic variables should have some “spread”: i.e. what we call 0 and 1 are never precisely 0 and 1, but instead are some region around magnetization all pointing in one direction and some region around magnetization pointing in another direction.
I really like the objections raised in these articles. But I’m not convinced that either side has won this battle. One interesting thing which I note is that the argument against Laundauer’s principle treats the two macrostates 0 and 1 in a very “error-free” manner. That is to say they treat these variables are really digital values. But (one last heresy!) I’m inclided to believe that nothing is perfectly digital. The digital nature of information in the physical world is an amazingly good approximation for computers….but it does fail. If you were able to precisely measure the information stored on your hard drive, you would not find zeros and ones, but instead zeros plus some small fluctuation and ones plus some small fluctuations. Plus, if there is ever an outside environment which is influencing the variable you are measuring, then it is certainly true that eventually your information, in thermodynamics, will disappear (see my previous article on Toom’s rule for hints as to why this should be so.) So in that case, the claim that these two bit states should never be accessible to each other, clearly breaks down. So I’m a bit worried (1) about the arguments against Laundauer’s principle from the point of view that digital information is only an approximation, but also (2) about arguements for Laundauer’s principle and the fact that they might somehow depend on how one completes thermodynamics to talk about multiple eqiulibria.
Of course, there is also the question of how all this works for quantum systems. But then we’d have to get into what quantum thermodynamics means, and well, that’s a battle for another day!
Update: be sure to read Cris Moore’s take on these two papers in the comment section. One thing I didn’t talk about was the example Shenker used against Laundauer’s principle. This was mostly because I didn’t understand it well enough and reading Cris’s comments, I agree with him that this counterexample seems to have problems.

Teaching Myself to Teach

A confession. When I was an undergraduate, I really didn’t go to classes as much as I should have. In particular, I didn’t go to many of my physics and math classes. Why? Mostly because these were the classes where I had the least problem picking up the material and my own self-motivation to learn the material on my own was almost always enough to carry me through the class. So the classes I attended the most were my humanities classes (to get that valuable B.S. in literature 😉 ) and my elective scientific courses, particularly the courses I took in computational neural science. Now that I’ve started teaching my own class, I wonder how much my own teaching suffers because of my past habits skipping class?
One thing I’ve noticed is that I have a hard time lecturing around a textbook. I have this insane desire to explain the subject material in my own words, and following a textbook closely makes me feel like I am not much more than a glorified parrot. I think, perhaps, this just has to do with my own proximity to the subject matter I’m teaching, quantum computing. Teaching about a subject which is not your own field of research in some ways seems like it would be easier, because you feel less of a desire to make sure you get it just right.
I am also fighting, in my own teaching, the three subject structure which was prevalent at Caltech. The three subject structure was the expectation that a course would involve lectures on one part of the subject, homeworks on another, and tests on a third part. Of course there was always some overlap, but there was also a lot of “thrown them into the fire” homeworks and tests. I can’t count the number of times I realized halfway through a test, “Oh, so this is what that means!” Fine for self-motivated self-learners, but not the best for everyone else.
Anyway, I think I’m slowly beginning to learn to teach. The first lecture for the course, I, uh, how to put this nicely, well I totally misjudge the appropriate difficulty level I should have been shooting for in the classs. In particular, I’m teaching the course to professional master’s students in computer science, some of whom have been out of school for a few years, and so, while I may want to teach them quantum theory in one lecture, this just is not very realistic! So starting in lecture two, we slowed down the pace quite a bit. Now entering into the fifth lecture, we’re beginning to get to things which I would consider truely quantum computing subjects. Now the challenge will be, again, to restrain myself from running rampshot through this material. Certainly there last homework was significantly harder than their first two, so I’m really trying to slow the pace as we enter the really cools stuff in quantum computing. Interestingly, slowing down has resulted in, I think, teaching the material in a careful manner, but has also kept me from teaching the big picture as effectively as possible. Mixing these two styles, big picture and instruction on the details, is something I’m working on.
Well, back to working on my lecture and the next homework set!

Happy Cow Day!

Which is more disturbing, the fact that today is “Cow Appreciation Day” (and here I thought July 14th was just “Recover from Bastille Day Day”) or the fact that “Chick-fil-A” will award a free combo meal to anyone who comes to one of their 1200 plus restraunts on Cow Appreciation Day “fully dressed as a cow”