## Archive for the ‘Adventures in Meatspace’ Category

### Amsterdam art museums plagiarizing my blog?

Wednesday, July 12th, 2017

This past week I had the pleasure of attending COLT (Conference on Learning Theory) 2017 in Amsterdam, and of giving an invited talk on “PAC-Learning and Reconstruction of Quantum States.”  You can see the PowerPoint slides here; videos were also made, but don’t seem to be available yet.

This was my first COLT, but almost certainly not the last.  I learned lots of cool new tidbits, from the expressive power of small-depth neural networks, to a modern theoretical computer science definition of “non-discriminatory” (namely, your learning algorithm’s output should be independent of protected categories like race, sex, etc. after conditioning on the truth you’re trying to predict), to the inapproximability of VC dimension (assuming the Exponential Time Hypothesis).  You can see the full schedule here.  Thanks so much to the PC chairs, Ohad Shamir and Satyen Kale, for inviting me and for putting on a great conference.

And one more thing: I’m not normally big on art museums, but Amsterdam turns out to have two in close proximity to each other—the Rijksmuseum and the Stedelijk—each containing something that Shtetl-Optimized readers might recognize.

Photo credits: Ronald de Wolf and Marijn Heule.

### Thoughts on the murderer outside my building

Tuesday, May 2nd, 2017

Any comments on the horrific stabbing at UT Austin yesterday? Were you anywhere near the festivities? Does this modify your position on open carry of firearms by students and faculty?

I was in the CS building (the Gates Dell Complex) at the time, which is about a 3-minute walk down Speedway from where the stabbings occurred.  I found about it a half hour later, as I was sitting in the student center eating.  I then walked outside to find the police barricades and hordes of students on their phones, reassuring their parents and friends that they were OK.

The plaza where it happened is one that I walk through every week—often to take Lily swimming in the nearby Gregory Gym.  (Lily’s daycare is also a short walk from where the stabbings were.)

Later in the afternoon, I walked Lily home in her stroller, through a campus that was nearly devoid of pedestrians.  Someone pulled up to me in his car, to ask whether I knew what had happened—as if he couldn’t believe that anyone who knew would nevertheless be walking around outside, Bayesian considerations be damned.  I said that I knew, and it was awful.  I then continued home.

What can one say about something so gruesome and senseless?  Other than that my thoughts are with the victims and their families, I hope and expect that the perpetrator receives justice, and I hope but don’t expect that nothing like this ever happens again, on this campus or on any other. I’m not going to speculate about the perpetrator’s motives; I trust the police and detectives to do their work.  (As one my colleagues put it: “it seems like clearly some sort of hate crime, but who exactly did he hate, and why?”)

And no, this doesn’t change my feelings about “campus carry” in any way. Note, in particular, that no armed student did stop the stabber, in the two minutes or so that he was on the loose—though some proponents of campus carry so badly wanted to believe that’s what happened, that they circulated the false rumor on Twitter that it had.  In reality, the stabber was stopped by an armed cop.

Yes, if UT Austin had been like an Israeli university, with students toting firearms and carefully trained in their use, it’s possible that one of those students would’ve stopped the lunatic.  But without universal military service, why would the students be suitably trained?  Given the gun culture in the US, and certainly the gun culture in Texas, isn’t it overwhelmingly likelier that a gun-filled campus would lead to more such tragedies, and those on a larger scale?  I’d rather see UT respond to this detestable crime—and others, like the murder of Haruka Weiser last year—with a stronger police presence on campus.

Other than that, life goes on.  Classes were cancelled yesterday from ~3PM onward, but they resumed today.  I taught this afternoon, giving my students one extra day to turn in their problem set.  I do admit that I slightly revised my lecture, which was about the Gottesman-Knill Theorem, so that it no longer used the notation Stab(|ψ⟩) for the stabilizer group of a quantum state |ψ⟩.

### Me at the Science March today, in front of the Texas Capitol in Austin

Saturday, April 22nd, 2017

### Daniel Moshe Aaronson

Saturday, March 25th, 2017

Born Wednesday March 22, 2017, exactly at noon.  19.5 inches, 7 pounds.

I learned that Dana had gone into labor—unexpectedly early, at 37 weeks—just as I was waiting to board a redeye flight back to Austin from the It from Qubit complexity workshop at Stanford.  I made it in time for the birth with a few hours to spare.  Mother and baby appear to be in excellent health.  So far, Daniel seems to be a relatively easy baby.  Lily, his sister, is extremely excited to have a new playmate (though not one who does much yet).

I apologize that I haven’t been answering comments on the is-the-universe-a-simulation thread as promptly as I normally do.  This is why.

### A day to celebrate

Friday, January 20th, 2017

Today—January 20, 2017—I have something cheerful, something that I’m celebrating.  It’s Lily’s fourth birthday. Happy birthday Lily!

As part of her birthday festivities, and despite her packed schedule, Lily has graciously agreed to field a few questions from readers of this blog.  You can ask about her parents, favorite toys, recent trip to Disney World, etc.  Just FYI: to the best of my knowledge, Lily doesn’t have any special insight about computational complexity, although she can write the letters ‘N’ and ‘P’ and find them on the keyboard.  Nor has she demonstrated much interest in politics, though she’s aware that many people are upset because a very bad man just became the president.  Anyway, if you ask questions that are appropriate for a real 4-year-old girl, rather than a blog humor construct, there’s a good chance I’ll let them through moderation and pass them on to her!

Meanwhile, here’s a photo I took of UT Austin students protesting Trump’s inauguration beneath the iconic UT tower.

### The teaser

Tuesday, December 13th, 2016

Tomorrow, I’ll have something big to announce here.  So, just to whet your appetites, and to get myself back into the habit of blogging, I figured I’d offer you an appetizer course: some more miscellaneous non-Trump-related news.

(1) My former student Leonid Grinberg points me to an astonishing art form, which I somehow hadn’t known about: namely, music videos generated by executable files that fit in only 4K of memory.  Some of these videos have to be seen to be believed.  (See also this one.)  Much like, let’s say, a small Turing machine whose behavior is independent of set theory, these videos represent exercises in applied (or, OK, recreational) Kolmogorov complexity: how far out do you need to go in the space of all computer programs before you find beauty and humor and adaptability and surprise?

Admittedly, Leonid explains to me that the rules allow these programs to call DirectX and Visual Studio libraries to handle things like the 3D rendering (with the libraries not counted toward the 4K program size).  This makes the programs’ existence merely extremely impressive, rather than a sign of alien superintelligence.

In some sense, all the programming enthusiasts over the decades who’ve burned their free time and processor cycles on Conway’s Game of Life and the Mandelbrot set and so forth were captivated by the same eerie beauty showcased by the videos: that of data compression, of the vast unfolding of a simple deterministic rule.  But I also feel like the videos add a bit extra: the 3D rendering, the music, the panning across natural or manmade-looking dreamscapes.  What we have here is a wonderful resource for either an acid trip or an undergrad computability and complexity course.

(2) A week ago Igor Oliveira, together with my longtime friend Rahul Santhanam, released a striking paper entitled Pseudodeterministic Constructions in Subexponential Time.  To understand what this paper does, let’s start with Terry Tao’s 2009 polymath challenge: namely, to find a fast, deterministic method that provably generates large prime numbers.  Tao’s challenge still stands today: one of the most basic, simplest-to-state unsolved problems in algorithms and number theory.

To be clear, we already have a fast deterministic method to decide whether a given number is prime: that was the 2002 breakthrough by Agrawal, Kayal, and Saxena.  We also have a fast probabilistic method to generate large primes: namely, just keep picking n-digit numbers at random, test each one, and stop when you find one that’s prime!  And those methods can be made deterministic assuming far-reaching conjectures in number theory, such as Cramer’s Conjecture (though note that even the Riemann Hypothesis wouldn’t lead to a polynomial-time algorithm, but “merely” a faster exponential-time one).

But, OK, what if you want a 5000-digit prime number, and you want it now: provably, deterministically, and fast?  That was Tao’s challenge.  The new paper by Oliveira and Santhanam doesn’t quite solve it, but it makes some exciting progress.  Specifically, it gives a deterministic algorithm to generate n-digit prime numbers, with merely the following four caveats:

• The algorithm isn’t polynomial time, but subexponential (2n^o(1)) time.
• The algorithm isn’t deterministic, but pseudodeterministic (a concept introduced by Gat and Goldwasser).  That is, the algorithm uses randomness, but it almost always succeeds, and it outputs the same n-digit prime number in every case where it succeeds.
• The algorithm might not work for all input lengths n, but merely for infinitely many of them.
• Finally, the authors can’t quite say what the algorithm is—they merely prove that it exists!  If there’s a huge complexity collapse, such as ZPP=PSPACE, then the algorithm is one thing, while if not then the algorithm is something else.

Strikingly, Oliveira and Santhanam’s advance on the polymath problem is pure complexity theory: hitting sets and pseudorandom generators and win-win arguments and stuff like that.  Their paper uses absolutely nothing specific to the prime numbers, except the facts that (a) there are lots of them (the Prime Number Theorem), and (b) we can efficiently decide whether a given number is prime (the AKS algorithm).  It seems almost certain that one could do better by exploiting more about primes.

(3) I’m in Lyon, France right now, to give three quantum computing and complexity theory talks.  I arrived here today from London, where I gave another two lectures.  So far, the trip has been phenomenal, my hosts gracious, the audiences bristling with interesting questions.

But getting from London to Lyon also taught me an important life lesson that I wanted to share: never fly EasyJet.  Or at least, if you fly one of the European “discount” airlines, realize that you get what you pay for (I’m told that Ryanair is even worse).  These airlines have a fundamentally dishonest business model, based on selling impossibly cheap tickets, but then forcing passengers to check even tiny bags and charging exorbitant fees for it, counting on snagging enough travelers who just naïvely clicked “yes” to whatever would get them from point A to point B at a certain time, assuming that all airlines followed more-or-less similar rules.  Which might not be so bad—it’s only money—if the minuscule, overworked staff of these quasi-airlines didn’t also treat the passengers like beef cattle, barking orders and berating people for failing to obey rules that one could log hundreds of thousands of miles on normal airlines without ever once encountering.  Anyway, if the airlines won’t warn you, then Shtetl-Optimized will.

### My 5-minute quantum computing talk at the White House

Tuesday, October 25th, 2016

(OK, technically it was in the Eisenhower Executive Office Building, which is not exactly the White House itself, but is adjacent to the West Wing in the White House complex.  And President Obama wasn’t there—maybe, like Justin Trudeau, he already knows everything about quantum computing?  But lots of people from the Office of Science and Technology Policy were!  And some of us talked with Valerie Jarrett, Obama’s adviser, when she passed us on her way to the West Wing.

The occasion was a Quantum Information Science policy workshop that OSTP held, and which the White House explicitly gave us permission to discuss on social media.  Indeed, John Preskill already tweeted photos from the event.  Besides me and Preskill, others in attendance included Umesh Vazirani, Seth Lloyd, Yaoyun Shi, Rob Schoelkopf, Krysta Svore, Hartmut Neven, Stephen Jordan…

I don’t know whether this is the first time that the polynomial hierarchy, or the notion of variation distance, were ever invoked in a speech at the White House.  But in any case, I was proud to receive a box of Hershey Kisses bearing the presidential seal.  I thought of not eating them, but then I got hungry, and realized that I can simply refill the box later if desired.

For regular readers of Shtetl-Optimized, my talk won’t have all that much that’s new, but in any case it’s short.

Incidentally, during the workshop, a guy from OSTP told me that, when he and others at the White House were asked to prepare materials about quantum computing, posts on Shtetl-Optimized (such as Shor I’ll Do It) were a huge help.  Honored though I was to have “served my country,” I winced, thinking about all the puerile doofosities I might’ve self-censored had I had any idea who might read them.  I didn’t dare ask whether anyone at the White House also reads the comment sections!

Thanks so much to all the other participants and to the organizers for a great workshop.  –SA)

Quantum Supremacy

by Scott Aaronson (UT Austin)

October 18, 2016

Thank you; it’s great to be here.  There are lots of directions that excite me enormously right now in quantum computing theory, which is what I work on.  For example, there’s the use of quantum computing to get new insight into classical computation, into condensed matter physics, and recently, even into the black hole information problem.

But since I have five minutes, I wanted to talk here about one particular direction—one that, like nothing else that I know of, bridges theory and experiment in the service of what we hope will be a spectacular result in the near future.  This direction is what’s known as “Quantum Supremacy”—John [Preskill], did you help popularize that term?  [John nods yes]—although some people have been backing away from the term recently, because of the campaign of one of the possible future occupants of this here complex.

But what quantum supremacy means to me, is demonstrating a quantum speedup for some task as confidently as possible.  Notice that I didn’t say a useful task!  I like to say that for me, the #1 application of quantum computing—more than codebreaking, machine learning, or even quantum simulation—is just disproving the people who say quantum computing is impossible!  So, quantum supremacy targets that application.

What is important for quantum supremacy is that we solve a clearly defined problem, with some relationship between inputs and outputs that’s independent of whatever hardware we’re using to solve the problem.  That’s part of why it doesn’t cut it to point to some complicated, hard-to-simulate molecule and say “aha!  quantum supremacy!”

One discovery, which I and others stumbled on 7 or 8 years ago, is that quantum supremacy seems to become much easier to demonstrate if we switch from problems with a single valid output to sampling problems: that is, problems of sampling exactly or approximately from some specified probability distribution.

Doing this has two advantages.  First, we no longer need a full, fault-tolerant quantum computer—in fact, very rudimentary types of quantum hardware appear to suffice.  Second, we can design sampling problems for which we can arguably be more confident that they really are hard for a classical computer, than we are that (say) factoring is classically hard.  I like to say that a fast classical factoring algorithm might collapse the world’s electronic commerce, but as far as we know, it wouldn’t collapse the polynomial hierarchy!  But with sampling problems, at least with exact sampling, we can often show the latter implication, which is about the best evidence you can possibly get for such a problem being hard in the present state of mathematics.

One example of these sampling tasks that we think are classically hard is BosonSampling, which Alex Arkhipov and I proposed in 2011.  BosonSampling uses a bunch of identical photons that are sent through a network of beamsplitters, then measured to count the number of photons in each output mode.  Over the past few years, this proposal has been experimentally demonstrated by quantum optics groups around the world, with the current record being a 6-photon demonstration by the O’Brien group in Bristol, UK.  A second example is the IQP (“Instantaneous Quantum Polynomial-Time”) or Commuting Hamiltonians model of Bremner, Jozsa, and Shepherd.

A third example—no doubt the simplest—is just to sample from the output distribution of a random quantum circuit, let’s say on a 2D square lattice of qubits with nearest-neighbor interactions.  Notably, this last task is one that the Martinis group at Google is working toward achieving right now, with 40-50 qubits.  They say that they’ll achieve it in as little as one or two years, which translated from experimental jargon, means maybe five years?  But not infinity years.

The challenges on the experimental side are clear: get enough qubits with long enough coherence times to achieve this.  But there are also some huge theoretical challenges remaining.

A first is, can we still solve classically hard sampling problems even in the presence of realistic experimental imperfections?  Arkhipov and I already thought about that problem—in particular, about sampling from a distribution that’s merely close in variation distance to the BosonSampling one—and got results that admittedly weren’t as satisfactory as the results for exact sampling.  But I’m delighted to say that, just within the last month or two, there have been some excellent new papers on the arXiv that tackle exactly this question, with both positive and negative results.

A second theoretical challenge is, how do we verify the results of a quantum supremacy experiment?  Note that, as far as we know today, verification could itself require classical exponential time.  But that’s not the showstopper that some people think, since we could target the “sweet spot” of 40-50 qubits, where classical verification is difficult (and in particular, clearly “costlier” than running the experiment itself), but also far from impossible with cluster computing resources.

If I have any policy advice, it’s this: recognize that a clear demonstration of quantum supremacy is at least as big a deal as (say) the discovery of the Higgs boson.  After this scientific milestone is achieved, I predict that the whole discussion of commercial applications of quantum computing will shift to a new plane, much like the Manhattan Project shifted to a new plane after Fermi built his pile under the Chicago stadium in 1942.  In other words: at this point, the most “applied” thing to do might be to set applications aside temporarily, and just achieve this quantum supremacy milestone—i.e., build the quantum computing Fermi pile—and thereby show the world that quantum computing speedups are a reality.  Thank you.

### Avi Wigderson’s “Permanent” Impact on Me

Wednesday, October 12th, 2016

The following is the lightly-edited transcript of a talk that I gave a week ago, on Wednesday October 5, at Avi Wigderson’s 60th birthday conference at the Institute for Advanced Study in Princeton.  Videos of all the talks (including mine) are now available here.

Thanks so much to Sanjeev Arora, Boaz Barak, Ran Raz, Peter Sarnak, and Amir Shpilka for organizing the conference and for inviting me to speak; to all the other participants and speakers for a great conference; and of course to Avi himself for being Avi. –SA

I apologize that I wasn’t able to prepare slides for today’s talk. But the good news is that, ever since I moved to Texas two months ago, I now carry concealed chalk everywhere I go. [Pull chalk out of pocket]

My history with Avi goes back literally half my life. I spent a semester with him at Hebrew University, and then a year with him as a postdoc here at IAS. Avi has played a bigger role in my career than just about anyone—he helped me professionally, he helped me intellectually, and once I dated and then married an Israeli theoretical computer scientist (who was also a postdoc in Avi’s group), Avi even helped me learn Hebrew. Just today, Avi taught me the Hebrew word for the permanent of a matrix. It turns out that it’s [throaty noises] pehhrmahnent.

But it all started with a talk that Avi gave in Princeton in 2000, which I attended as a prospective graduate student. That talk was about the following function of an n×n matrix A∈Rn×n, the permanent:

$$\text{Per}(A) = \sum_{\sigma \in S_n} \prod_{i=1}^n a_{i,\sigma(i)}.$$

Avi contrasted that function with a superficially similar function, the determinant:

$$\text{Det}(A) = \sum_{\sigma \in S_n} (-1)^{\text{sgn}(\sigma)} \prod_{i=1}^n a_{i,\sigma(i)}.$$

In this talk, I want to share a few of the amazing things Avi said about these two functions, and how the things he said then reverberated through my entire career.

Firstly, like we all learn in kindergarten or whatever, the determinant is computable in polynomial time, for example by using Gaussian elimination. By contrast, Valiant proved in 1979 that computing the permanent is #P-complete—which means, at least as hard as any combinatorial counting problem, a class believed to be even harder than NP-complete.

So, despite differing from each other only by some innocent-looking -1 factors, which the determinant has but the permanent lacks, these two functions effectively engage different regions of mathematics. The determinant is linear-algebraic and geometric; it’s the product of the eigenvalues and the volume of the parallelipiped defined by the row vectors. But the permanent is much more stubbornly combinatorial. It’s not quite as pervasive in mathematics as the determinant is, though it does show up. As an example, if you have a bipartite graph G, then the permanent of G’s adjacency matrix counts the number of perfect matchings in G.

When n=2, computing the permanent doesn’t look too different from computing the determinant: indeed, we have

$$\text{Per}\left( \begin{array} [c]{cc}% a & b\\ c & d \end{array} \right) =\text{Det}\left( \begin{array} [c]{cc}% a & -b\\ c & d \end{array} \right).$$

But as n gets larger, the fact that the permanent is #P-complete means that it must get exponentially harder to compute than the determinant, unless basic complexity classes collapse. And indeed, to this day, the fastest known algorithm to compute an n×n permanent, Ryser’s algorithm, takes O(n2n) time, which is only modestly better than the brute-force algorithm that just sums all n! terms.

Yet interestingly, not everything about the permanent is hard. So for example, if A is nonnegative, then in 1997, Jerrum, Sinclair, and Vigoda famously gave a polynomial-time randomized algorithm to approximate Per(A) to within a 1+ε multiplicative factor, for ε>0 as small as you like. As an even simpler example, if A is nonnegative and you just want to know whether its permanent is zero or nonzero, that’s equivalent to deciding whether a bipartite graph has at least one perfect matching. And we all know that that can be done in polynomial time.

OK, but the usual algorithm from the textbooks that puts the matching problem in the class P is already a slightly nontrivial one—maybe first grade rather than kindergarten! It involves repeatedly using depth-first search to construct augmenting paths, then modifying the graph, etc. etc.

Sixteen years ago in Princeton, the first thing Avi said that blew my mind is that there’s a vastly simpler polynomial-time algorithm to decide whether a bipartite graph has a perfect matching—or equivalently, to decide whether a nonnegative matrix has a zero or nonzero permanent. This algorithm is not quite as efficient as the textbook one, but it makes up for it by being more magical.

So here’s what you do: you start with the 0/1 adjacency matrix of your graph. I’ll do a 2×2 example, since that’s all I’ll be able to compute on the fly:

$$\left( \begin{array} [c]{cc}% 1 & 1\\ 0 & 1 \end{array} \right).$$

Then you normalize each row so it sums to 1. In the above example, this would give

$$\left( \begin{array} [c]{cc}% \frac{1}{2} & \frac{1}{2} \\ 0 & 1 \end{array} \right).$$

Next you normalize each column so it sums to 1:

$$\left( \begin{array} [c]{cc}% 1 & \frac{1}{3} \\ 0 & \frac{2}{3} \end{array} \right).$$

OK, but now the problem is that the rows are no longer normalized, so you normalize them again:

$$\left( \begin{array} [c]{cc}% \frac{3}{4} & \frac{1}{4} \\ 0 & 1 \end{array} \right).$$

Then you normalize the columns again, and so on. You repeat something like n3log(n) times. If, after that time, all the row sums and column sums have become within ±1/n of 1, then you conclude that the permanent was nonzero and the graph had a perfect matching. Otherwise, the permanent was zero and the graph had no perfect matching.

What gives? Well, it’s a nice exercise to prove why this works. I’ll just give you a sketch: first, when you multiply any row or column of a matrix by a scalar, you multiply the permanent by that same scalar. By using that fact, together with the arithmetic-geometric mean inequality, it’s possible to prove that, in every iteration but the first, when you rebalance all the rows or all the columns to sum to 1, you can’t be decreasing the permanent. The permanent increases monotonically.

Second, if the permanent is nonzero, then after the first iteration it’s at least 1/nn, simply because we started with a matrix of 0’s and 1’s.

Third, the permanent is always at most the product of the row sums or the product of the column sums, which after the first iteration is 1.

Fourth, at any iteration where there’s some row sum or column sum that’s far from 1, rescaling must not only increase the permanent, but increase it by an appreciable amount—like, a 1+1/n2 factor or so.

Putting these four observations together, we find that if the permanent is nonzero, then our scaling procedure must terminate after a polynomial number of steps, with every row sum and every column sum close to 1—since otherwise, the permanent would just keep on increasing past its upper bound of 1.

But a converse statement is also true. Suppose the matrix can be scaled so that every row sum and every column sum gets within ±1/n of 1. Then the rescaled entries define a flow through the bipartite graph, with roughly the same amount of flow through each of the 2n vertices. But if such a flow exists, then Hall’s Theorem tells you that there must be a perfect matching (and hence the permanent must be nonzero)—since if a matching didn’t exist, then there would necessarily be a bottleneck preventing the flow.

Together with Nati Linial and Alex Samorodnitsky, Avi generalized this to show that scaling the rows and columns gives you a polynomial-time algorithm to approximate the permanent of a nonnegative matrix. This basically follows from the so-called Egorychev-Falikman Theorem, which says that the permanent of a doubly stochastic matrix is at least n!/nn. The approximation ratio that you get this way, ~en, isn’t nearly as good as Jerrum-Sinclair-Vigoda’s, but the advantage is that the algorithm is deterministic (and also ridiculously simple).

For myself, though, I just filed away this idea of matrix scaling for whenever I might need it. It didn’t take long. A year after Avi’s lecture, when I was a beginning grad student at Berkeley, I was obsessing about the foundations of quantum mechanics. Specifically, I was obsessing about the fact that, when you measure a quantum state, the rules of quantum mechanics tell you how to calculate the probability that you’ll see a particular outcome. But the rules are silent about so-called multiple-time or transition probabilities. In other words: suppose we adopt Everett’s Many-Worlds view, according to which quantum mechanics needs to be applied consistently to every system, regardless of scale, so in particular, the state of the entire universe (including us) is a quantum superposition state. We perceive just one branch, but there are also these other branches where we made different choices or where different things happened to us, etc.

OK, fine: for me, that’s not the troubling part! The troubling part is that quantum mechanics rejects as meaningless questions like the following: given that you’re in this branch of the superposition at time t1, what’s the probability that you’ll be in that branch at time t2, after some unitary transformation is applied? Orthodox quantum mechanics would say: well, either someone measured you at time t1, in which case their act of measuring collapsed the superposition and created a whole new situation. Or else no one measured at t1—but in that case, your state at time t1 was the superposition state, full stop. It’s sheer metaphysics to imagine a “real you” that jumps around from one branch of the superposition to another, having a sequence of definite experiences.

Granted, in practice, branches of the universe’s superposition that split from each other tend never to rejoin, for the same thermodynamic reasons why eggs tend never to unscramble themselves. And as long as the history of the Everettian multiverse has the structure of a tree, we can sensibly define transition probabilities. But if, with some technology of the remote future, we were able to do quantum interference experiments on human brains (or other conscious entities), the rules of quantum mechanics would no longer predict what those beings should see—not even probabilistically.

I was interested in the question: suppose we just wanted to postulate transition probabilities, with the transitions taking place in some fixed orthogonal basis. What would be a mathematically reasonable way to do that? And it occurred to me that one thing you could do is the following. Suppose for simplicity that you have a pure quantum state, which is just a unit vector of n complex numbers called amplitudes:

$$\left( \begin{array} [c]{c}% \alpha_{1}\\ \vdots\\ \alpha_{n}% \end{array} \right)$$

Then the first rule of quantum mechanics says that you can apply any unitary transformation U (that is, any norm-preserving linear transformation) to map this state to a new one:

$$\left( \begin{array} [c]{c}% \beta_{1}\\ \vdots\\ \beta_{n}% \end{array} \right) =\left( \begin{array} [c]{ccc}% u_{11} & \cdots & u_{1n}\\ \vdots & \ddots & \vdots\\ u_{n1} & \cdots & u_{nn}% \end{array} \right) \left( \begin{array} [c]{c}% \alpha_{1}\\ \vdots\\ \alpha_{n}% \end{array} \right).$$

The second rule of quantum mechanics, the famous Born Rule, says that if you measure in the standard basis before applying U, then the probability that you’ll find youself in state i equals |αi|2. Likewise, if you measure in the standard basis after applying U, the probability that you’ll find youself in state j equals |βj|2.

OK, but what’s the probability that you’re in state i at the initial time, and then state j at the final time? These joint probabilities, call them pij, had better add up to |αi|2 and |βj|2, if we sum the rows and columns respectively. And ideally, they should be “derived” in some way from the unitary U—so that for example, if uij=0 then pij=0 as well.

So here’s something you could do: start by replacing each uij by its absolute value, to get a nonnegative matrix. Then, normalize the ith row so that it sums to |αi|2, for each i. Then normalize the jth column so that it sums to |βj|2, for each j. Then normalize the rows, then the columns, and keep iterating until hopefully you end up with all the rows and columns having the right sums.

So the first question I faced was, does this process converge? And I remembered what Avi taught me about the permanent. In this case, because of the nonuniform row and column scalings, the permanent no longer works as a progress measure, but there’s something else that does work. Namely, as a first step, we can use the Max-Flow/Min-Cut Theorem to show that there exists a nonnegative matrix F=(fij) such that fij=0 whenever uij=0, and also

$$\sum_j f_{ij} = \left|\alpha_i\right|^2 \forall i,\ \ \ \ \ \sum_i f_{ij} = \left|\beta_j\right|^2 \forall j.$$

Then, letting M=(mij) be our current rescaled matrix (so that initially mij:=|uij|), we use

$$\prod_{i,j : u_{ij}\ne 0} m_{ij}^{f_{ij}}$$

as our progress measure. By using the nonnegativity of the Kullback-Leibler divergence, one can prove that this quantity never decreases. So then, just like with 0/1 matrices and the permanent, we get eventual convergence, and indeed convergence after a number of iterations that’s polynomial in n.

I was pretty stoked about this until I went to the library, and discovered that Erwin Schrödinger had proposed the same matrix scaling process in 1931! And Masao Nagasawa and others then rigorously analyzed it. OK, but their motivations were somewhat different, and for some reason they never talked about finite-dimensional matrices, only infinite-dimensional ones.

I can’t resist telling you my favorite open problem about this matrix scaling process: namely, is it stable under small perturbations? In other words, if I change one of the αi‘s or uij‘s by some small ε, then do the final pij‘s also change by at most some small δ? To clarify, several people have shown me how to prove that the mapping to the pij‘s is continuous. But for computer science applications, one needs something stronger: namely that when the matrix M, and the row and column scalings, actually arise from a unitary matrix in the way above, we get strong uniform continuity, with a 1/nO(1) change to the inputs producing only a 1/nO(1) change to the outputs (and hopefully even better than that).

The more general idea that I was groping toward or reinventing here is called a hidden-variable theory, of which the most famous example is Bohmian mechanics. Again, though, Bohmian mechanics has the defect that it’s only formulated for some exotic state space that the physicists care about for some reason—a space involving pointlike objects called “particles” that move around in 3 Euclidean dimensions (why 3? why not 17?).

Anyway, this whole thing led me to wonder: under the Schrödinger scaling process, or something like it, what’s the computational complexity of sampling an entire history of the hidden variable through a quantum computation? (“If, at the moment of your death, your whole life history flashes before you in an instant, what can you then efficiently compute?”)

Clearly the complexity is at least BQP (i.e., quantum polynomial time), because even sampling where the hidden variable is at a single time is equivalent to sampling the output distribution of a quantum computer. But could the complexity be even more than BQP, because of the correlations between the hidden variable values at different times? I noticed that, indeed, sampling a hidden variable history would let you do some crazy-seeming things, like solve the Graph Isomorphism problem in polynomial time (OK, fine, that seemed more impressive at the time than it does after Babai’s breakthrough), or find collisions in arbitrary cryptographic hash functions, or more generally, solve any problem in the complexity class SZK (Statistical Zero Knowledge).

But you might ask: what evidence do we have that any these problems are hard even for garden-variety quantum computers? As many of you know, it’s widely conjectured today that NP⊄BQP—i.e., that quantum computers can’t solve NP-complete problems in polynomial time. And in the “black box” setting, where all you know how to do is query candidate solutions to your NP-complete problem to check whether they’re valid, it’s been proven that quantum computers don’t give you an exponential speedup: the best they can give is the square-root speedup of Grover’s algorithm.

But for these SZK problems, like finding collisions in hash functions, who the hell knows? So, this is the line of thought that led me to probably the most important thing I did in grad school, the so-called quantum lower bound for collision-finding. That result says that, if (again) your hash function is only accessible as a black box, then a quantum computer can provide at most a polynomial speedup over a classical computer for finding collisions in it (in this case, it turns out, at most a two-thirds power speedup). There are several reasons you might care about that, such as showing that one of the basic building blocks of modern cryptography could still be secure in a world with quantum computers, or proving an oracle separation between SZK and BQP. But my original motivation was just to understand how transition probabilities would change quantum computation.

The permanent has also shown up in a much more direct way in my work on quantum computation. If we go back to Avi’s lecture from 2000, a second thing he said that blew my mind was that apparently, or so he had heard, even the fundamental particles of the universe know something about the determinant and the permanent. In particular, he said, fermions—the matter particles, like the quarks and electrons in this stage—have transition amplitudes that are determinants of matrices. Meanwhile, bosons—the force-carrying particles, like the photons coming from the ceiling that let you see this talk—have transition amplitudes that are permanents of matrices.

Or as Steven Weinberg, one of the great physicists on earth, memorably put it in the first edition of his recent quantum mechanics textbook: “in the case of bosons, it is also a determinant, except without minus signs.” I’ve had the pleasure of getting to know Weinberg at Austin, so recently I asked him about that line. He told me that of course he knew that the determinant without minus signs is called a permanent, but he thought no one else would know! As far as he knew, the permanent was just some esoteric function used by a few quantum field theorists who needed to calculate boson amplitudes.

Briefly, the reason why the permanent and determinant turn up here is the following: whenever you have n particles that are identical, to calculate the amplitude for them to do something, you need to sum over all n! possible permutations of the particles. Furthermore, each contribution to the sum is a product of n complex numbers, one uij for each particle that hops from i to j. But there’s a difference: when you swap two identical bosons, nothing happens, and that’s why bosons give rise to the permanent (of an n×n complex matrix, if there are n bosons). By contrast, when you swap two identical fermions, the amplitude for that state of the universe gets multiplied by -1, and that’s why fermions give rise to the determinant.

Anyway, Avi ended his talk with a quip about how unfair it seemed to the bosons that they should have to work so much harder than the fermions just to calculate where they should be!

And then that one joke of Avi—that way of looking at things—rattled around in my head for a decade, like a song I couldn’t get rid of. It raised the question: wait a minute, bosons—particles that occur in Nature—are governed by a #P-complete function? Does that mean we could actually use bosons to solve #P-complete problems in polynomial time? That seems ridiculous, like the kind of nonsense I’m fighting every few weeks on my blog! As I said before, most of us don’t even expect quantum computers to be able to solve NP-complete problems in polynomial time, let alone #P-complete ones.

As it happens, Troyansky and Tishby had already taken up that puzzle in 1996. (Indeed Avi, being the social butterfly and hub node of our field that he is, had learned about the role of permaments and determinants in quantum mechanics from them.) What Troyansky and Tishby said was, it’s true that if you have a system of n identical, non-interacting bosons, their transition amplitudes are given by permanents of n×n matrices. OK, but amplitudes in quantum mechanics are not directly observable. They’re just what you use to calculate the probability that you’ll see this or that measurement outcome. But if you try to encode a hard instance of a #P-complete problem into a bosonic system, the relevant amplitudes will in general be exponentially small. And that means that, if you want a decent estimate of the permanent, you’ll need to repeat the experiment an exponential number of times. So OK, they said, nice try, but this doesn’t give you a computational advantage after all in calculating the permanent compared to classical brute force.

In our 2011 work on BosonSampling, my student Alex Arkhipov and I reopened the question. We said, not so fast. It’s true that bosons don’t seem to help you in estimating the permanent of a specific matrix of your choice. But what if your goal was just to sample a random n×n matrix A∈Cn×n, in a way that’s somehow biased toward matrices with larger permanents? Now, why would that be your goal? I have no idea! But this sampling is something that a bosonic system would easily let you do.

So, what Arkhipov and I proved was that this gives rise to a class of probability distributions that can be sampled in quantum polynomial time (indeed, by a very rudimentary type of quantum computer), but that can’t be sampled in classical polynomial time unless the polynomial hierarchy collapses to the third level. And even though you’re not solving a #P-complete problem, the #P-completeness of the permanent still plays a crucial role in explaining why the sampling problem is hard. (Basically, one proves that the probabilities are #P-hard even to approximate, but that if there were a fast classical sampling algorithm, then the probabilities could be approximated in the class BPPNP. So if a fast classical sampling algorithm existed, then P#P would equal BPPNP, which would collapse the polynomial hierarchy by Toda’s Theorem.)

When we started on this, Arkhipov and I thought about it as just pure complexity theory—conceptually clarifying what role the #P-completeness of the permanent plays in physics. But then at some point it occurred to us: bosons (such as photons) actually exist, and experimentalists in quantum optics like to play with them, so maybe they could demonstrate some of this stuff in the lab. And as it turned out, the quantum optics people were looking for something to do at the time, and they ate it up.

Over the past five years, a trend has arisen in experimental physics that goes by the name “Quantum Supremacy,” although some people are now backing away from the name because of Trump. The idea is: without yet having a universal quantum computer, can we use the hardware that we’re able to build today to demonstrate the reality of a quantum-computational speedup as clearly as possible? Not necessarily for a useful problem, but just for some problem? Of course, no experiment can prove that something is scaling polynomially rather than exponentially, since that’s an asymptotic statement. But an experiment could certainly raise the stakes for the people who deny such a statement—for example, by solving something a trillion times faster than we know how to solve it otherwise, using methods for which we don’t know a reason for them not to scale.

I like to say that for me, the #1 application of quantum computing, more than breaking RSA or even simulating physics and chemistry, is simply disproving the people who say that quantum computing is impossible! So, quantum supremacy targets that application.

Experimental BosonSampling has become a major part of the race to demonstrate quantum supremacy. By now, at least a half-dozen groups around the world have reported small-scale implementations—the record, so far, being an experiment at Bristol that used 6 photons, and experimentally confirmed that, yes, their transition amplitudes are given by permanents of 6×6 complex matrices. The challenge now is to build single-photon sources that are good enough that you could scale up to (let’s say) 30 photons, which is where you’d really start seeing a quantum advantage over the best known classical algorithms. And again, this whole quest really started with Avi’s joke.

A year after my and Arkhipov’s work, I noticed that one also can run the connection between quantum optics and the permanent in the “reverse” direction. In other words: with BosonSampling, we used the famous theorem of Valiant, that the permanent is #P-complete, to help us argue that bosons can solve hard sampling problems. But if we know by some other means that quantum optics lets us encode #P-complete problems, then we can use that to give an independent, “quantum” proof that the permanent is #P-complete in the first place! As it happens, there is another way to see why quantum optics lets us encode #P-complete problems. Namely, we can use celebrated work by Knill, Laflamme, and Milburn (KLM) from 2001, which showed how to perform universal quantum computation using quantum optics with the one additional resource of “feed-forward measurements.” With minor modifications, the construction by KLM also lets us encode a #P-complete problem into a bosonic amplitude, which we know is a permanent—thereby proving that the permanent is #P-complete, in what I personally regard as a much more intuitive way than Valiant’s original approach based on cycle covers. This illustrates a theme that we’ve seen over and over in the last 13 years or so, which is the use of quantum methods and arguments to gain insight even about classical computation.

Admittedly, I wasn’t proving anything here in classical complexity theory that wasn’t already known, just giving a different proof for an old result! Extremely recently, however, my students Daniel Grier and Luke Schaeffer have extended my argument based on quantum optics, to show that computing the permanent of a unitary or orthogonal matrix is #P-complete. (Indeed, even over finite fields of characteristic k, computing the permanent of an orthogonal matrix is a ModkP-complete problem, as long as k is not 2 or 3—which turns out to be the tight answer.) This is not a result that we previously knew by any means, whether quantum or classical.

I can’t resist telling you the biggest theoretical open problem that arose from my and Arkhipov’s work. We would like to say: even if you had a polynomial-time algorithm that sampled a probability distribution that was merely close, in variation distance, to the BosonSampling distribution, that would already imply a collapse of the polynomial hierarchy. But we’re only able to prove that assuming a certain problem is #P-complete, which no one has been able to prove #P-complete. That problem is the following:

Given an n×n matrix A, each of whose entries is an i.i.d. complex Gaussian with mean 0 and variance 1 (that is, drawn from N(0,1)C), estimate |Per(A)|2, to within additive error ±ε·n!, with probability at least 1-δ over the choice of A. Do this in time polynomial in n, 1/ε, and 1/δ.

Note that, if you care about exactly computing the permanent of a Gaussian random matrix, or about approximating the permanent of an arbitrary matrix, we know how to prove both of those problems #P-complete. The difficulty “only” arises when we combine approximation and average-case in the same problem.

At the moment, we don’t even know something more basic, which is: what’s the distribution over |Per(A)|2, when A is an n×n matrix of i.i.d. N(0,1)C Gaussians? Based on numerical evidence, we conjecture that the distribution converges to lognormal as n gets large. By using the interpretation of the determinant as the volume of a parallelipiped, we can prove that the distribution over |Det(A)|2 converges to lognormal. And the distribution over |Per(A)|2 looks almost the same when you plot it. But not surprisingly, the permanent is harder to analyze.

This brings me to my final vignette. Why would anyone even suspect that approximating the permanent of a Gaussian random matrix would be a #P-hard problem? Well, because if you look at the permanent of an n×n matrix over a large enough finite field, say Fp, that function famously has the property of random self-reducibility. This means: the ability to calculate such a permanent in polynomial time, on 90% all matrices in Fpn×n, or even for that matter on only 1% of them, implies the ability to calculate it in polynomial time on every such matrix.

The reason for this is simply that the permanent is a low-degree polynomial, and low-degree polynomials have extremely useful error-correcting properties. In particular, if you can compute such a polynomial on any large fraction of points, then you can do noisy polynomial interpolation (e.g., the Berlekamp-Welch algorithm, or list decoding), in order to get the value of the polynomial on an arbitrary point.

I don’t specifically remember Avi talking about the random self-reducibility of the permanent in his 2000 lecture, but he obviously would have talked about it! And it was really knowing about the random self-reducibility of the permanent, and how powerful it was, that let me and Alex Arkhipov to the study of BosonSampling in the first place.

In complexity theory, the random self-reducibility of the permanent is hugely important because it was sort of the spark for some of our most convincing examples of non-relativizing results—that is, results that fail relative to a suitable oracle. The most famous such result is that #P, and for that matter even PSPACE, admit interactive protocols (the IP=PSPACE theorem). In the 1970s, Baker, Gill, and Solovay pointed out that non-relativizing methods would be needed to resolve P vs. NP and many of the other great problems of the field.

In 2007, Avi and I wrote our only joint paper so far. In that paper, we decided to take a closer look at the non-relativizing results based on interactive proofs. We said: while it’s true that these results don’t relativize—that is, there are oracles relative to which they fail—nevertheless, these results hold relative to all oracles that themselves encode low-degree polynomials over finite fields (such as the permanent). So, introducing a term, Avi and I said that results like IP=PSPACE algebrize.

But then we also showed that, if you want to prove P≠NP—or for that matter, even prove circuit lower bounds that go “slightly” beyond what’s already known (such as NEXPP/poly)—you’ll need techniques that are not only non-relativizing, but also non-algebrizing. So in some sense, the properties of the permanent that are used (for example) in proving that it has an interactive protocol, just “aren’t prying the black box open wide enough.”

I have a more recent result, from 2011 or so, that I never got around to finishing a paper about. In this newer work, I decided to take another look at the question: what is it about the permanent that actually fails to relativize? And I prove the following result: relative to an arbitrary oracle A, the class #P has complete problems that are both random self-reducible and downward self-reducible (that is, reducible to smaller instances of the same problem). So, contrary to what certainly I and maybe others had often thought, it’s not the random self-reducibility of the permanent that’s the crucial thing about it. What’s important, instead, is a further property that the permanent has, of being self-checkable and self-correctible.

In other words: given (say) a noisy circuit for the permanent, it’s not just that you can correct that circuit to compute whichever low-degree polynomial it was close to computing. Rather, it’s that you can confirm that the polynomial is in fact the permanent, and nothing else.

I like the way Ketan Mulmuley thinks about this phenomenon in his Geometric Complexity Theory, which is a speculative, audacious program to try to prove that the permanent is harder than the determinant, and to tackle the other great separation questions of complexity theory (including P vs. NP), by using algebraic geometry and representation theory. Mulmuley says: the permanent is a polynomial in the entries of an n×n matrix that not only satisfies certain symmetries (e.g., under interchanging rows or columns), but is uniquely characterized by those symmetries. In other words, if you find a polynomial that passes certain tests—for example, if it behaves in the right way under rescaling and interchanging rows and columns—then that polynomial must be the permanent, or a scalar multiple of the permanent. Similarly, if you find a polynomial that passes the usual interactive proof for the permanent, that polynomial must be the permanent. I think this goes a long way toward explaining why the permanent is so special: it’s not just any hard-to-compute, low-degree polynomial; it’s one that you can recognize when you come across it.

I’ve now told you about the eventual impact of one single survey talk that Avi gave 16 years ago—not even a particularly major or important one. So you can only imagine what Avi’s impact must have been on all of us, if you integrate over all the talks he’s given and papers he’s written and young people he’s mentored and connections he’s made his entire career. May that impact be permanent.

### Stuff That’s Happened

Sunday, October 9th, 2016

Hi from FOCS’2016 in scenic New Brunswick, NJ!  (I just got here from Avi Wigderson’s 60th birthday conference, to which I’ll devote another post.)

In the few weeks since I last overcame the activation barrier to blog, here are some things that happened.

Politics

Friday’s revelation, of Trump boasting on tape to George W. Bush’s cousin about his crotch-grabbing escapades, did not increase my opposition to Trump, for a very simple reason: because I’d already opposed Trump by the maximum amount that’s possible.  Nevertheless, I’ll be gratified if this news brings Trump down, and leads to the landslide defeat he’s deserved from the beginning for 101000 reasons.

Still, history (including the history of this election) teaches us not to take things for granted.  So if you’re still thinking of voting for Trump, let me recommend Scott Alexander’s endorsement of “anyone but Trump.”  I’d go even further than my fellow Scott A. in much of what he says, but his post is nevertheless a masterful document, demonstrating how someone who nobody could accuse of being a statist social-justice warrior, but who “merely” has a sense for science and history and Enlightenment ideals and the ironic and absurd, can reach the conclusion that Trump had better be stopped, and with huge argumentative margin to spare.

See also an interview with me on Huffington Post about TrumpTrading, conducted by Linchuan Zhang.  If you live in a swing state and support Johnson, or in a safe state and support Hillary, I still recommend signing up, since even a 13% probability of a Trump win is too high.  I’ve found a partner in Ohio, a libertarian-leaning professor.  The only way I can foresee not going through with the swap, is if the bus tape causes Trump’s popularity to drop so precipitously that Texas becomes competitive.

In the meantime, it’s also important that we remain vigilant about the integrity of the election—not about in-person voter fraud, which statistically doesn’t exist, but about intimidation at the polls and the purging of eligible voters and tampering with electronic voting machines.  As I’ve mentioned before on this blog, my childhood friend Alex Halderman, now a CS professor at the University of Michigan, has been at the forefront of demonstrating the security problems with electronic voting machines, and advocating for paper trails.  Alex and his colleagues have actually succeeded in influencing how elections are conducted in many states—but not in all of them.  If you want to learn more, check out an in-depth profile of Alex in the latest issue of Playboy.  (There’s no longer nudity in Playboy, so you can even read the thing at work…)

Now On To SCIENCE

As some of you probably saw, Mohammad Bavarian, Giulio Gueltrini, and I put out a new paper about computability theory in a universe with closed timelike curves.  This complements my and John Watrous’s earlier work about complexity theory in a CTC universe, where we showed that finding a fixed-point of a bounded superoperator is a PSPACE-complete problem.  In the new work, we show that finding a fixed-point of an unbounded superoperator has the same difficulty as the halting problem.

Some of you will also have seen that folks from the Machine Intelligence Research Institute (MIRI)—Scott Garrabrant, Tsvi Benson-Tilsen, Andrew Critch, Nate Soares, and Jessica Taylor—recently put out a major 130-page paper entitled “Logical Induction”.  (See also their blog announcement.)  This paper takes direct aim at a question that’s come up repeatedly in the comments section of this blog: namely, how can we sensibly assign probabilities to mathematical statements, such as “the 1010^1000th decimal digit of π is a 3″?  The paper proposes an essentially economic framework for that question, involving a marketplace for “mathematical truth futures,” in which new mathematical truths get revealed one by one, and one doesn’t want any polynomial-time traders to be able to make an infinite amount of money by finding patterns in the truths that the prices haven’t already factored in.  I won’t be able to do justice to the work in this paragraph (or even come close), but I hope this sophisticated paper gets the attention it deserves from mathematicians, logicians, CS theorists, AI people, economists, and anyone else who’s ever wondered how a “Bayesian” could sleep at night after betting on (say) the truth or falsehood of Goldbach’s Conjecture.  Feel free to discuss in the comments section.

My PhD student Adam Bouland and former visiting student Lijie Chen, along with Dhiraj Holden, Justin Thaler, and Prashant Vasudevan, have put out a new paper that achieves an oracle separation between the complexity classes SZK and PP (among many other things)—thereby substantially generalizing my quantum lower bound for the collision problem, and solving an open problem that I’d thought about without success since 2002.  Huge relativized congratulations to them!

A new paper by my PhD student Shalev Ben-David and Or Sattath, about using ideas from quantum money to create signed quantum tokens, has been making the rounds on social media.  Why?  Read the abstract and see for yourself!  (My only “contribution” was to tell them not to change a word.)

Several people wrote in to tell me about a recent paper by Henry Lin and Max Tegmark, which tries to use physics analogies and intuitions to explain why deep learning works as well as it does.  To my inexpert eyes, the paper seemed to contain a lot of standard insights from computational learning theory (for example, the need to exploit symmetries and regularities in the world to get polynomial-size representations), but expressed in a different language.  What confused me most was the paper’s claim to prove “no-flattening theorems” showing the necessity of large-depth neural networks—since in the sense I would mean, such a theorem couldn’t possibly be proved without a major breakthrough in computational complexity (e.g., separating the levels of the class TC0). Again, anyone who understands what’s going on is welcome to share in the comments section.

Sevag Gharibian asked me to advertise that the Call for Papers for the 2017 Conference on Computational Complexity, to be held July 6-9 in Riga, Latvia, is now up.

### The No-Cloning Theorem and the Human Condition: My After-Dinner Talk at QCRYPT

Monday, September 19th, 2016

The following are the after-dinner remarks that I delivered at QCRYPT’2016, the premier quantum cryptography conference, on Thursday Sep. 15 in Washington DC.  You could compare to my after-dinner remarks at QIP’2006 to see how much I’ve “”matured”” since then. Thanks so much to Yi-Kai Liu and the other organizers for inviting me and for putting on a really fantastic conference.

It’s wonderful to be here at QCRYPT among so many friends—this is the first significant conference I’ve attended since I moved from MIT to Texas. I do, however, need to register a complaint with the organizers, which is: why wasn’t I allowed to bring my concealed firearm to the conference? You know, down in Texas, we don’t look too kindly on you academic elitists in Washington DC telling us what to do, who we can and can’t shoot and so forth. Don’t mess with Texas! As you might’ve heard, many of us Texans even support a big, beautiful, physical wall being built along our border with Mexico. Personally, though, I don’t think the wall proposal goes far enough. Forget about illegal immigration and smuggling: I don’t even want Americans and Mexicans to be able to win the CHSH game with probability exceeding 3/4. Do any of you know what kind of wall could prevent that? Maybe a metaphysical wall.

OK, but that’s not what I wanted to talk about. When Yi-Kai asked me to give an after-dinner talk, I wasn’t sure whether to try to say something actually relevant to quantum cryptography or just make jokes. So I’ll do something in between: I’ll tell you about research directions in quantum cryptography that are also jokes.

The subject of this talk is a deep theorem that stands as one of the crowning achievements of our field. I refer, of course, to the No-Cloning Theorem. Almost everything we’re talking about at this conference, from QKD onwards, is based in some way on quantum states being unclonable. If you read Stephen Wiesner’s paper from 1968, which founded quantum cryptography, the No-Cloning Theorem already played a central role—although Wiesner didn’t call it that. By the way, here’s my #1 piece of research advice to the students in the audience: if you want to become immortal, just find some fact that everyone already knows and give it a name!

I’d like to pose the question: why should our universe be governed by physical laws that make the No-Cloning Theorem true? I mean, it’s possible that there’s some other reason for our universe to be quantum-mechanical, and No-Cloning is just a byproduct of that. No-Cloning would then be like the armpit of quantum mechanics: not there because it does anything useful, but just because there’s gotta be something under your arms.

OK, but No-Cloning feels really fundamental. One of my early memories is when I was 5 years old or so, and utterly transfixed by my dad’s home fax machine, one of those crappy 1980s fax machines with wax paper. I kept thinking about it: is it really true that a piece of paper gets transmaterialized, sent through a wire, and reconstituted at the other location? Could I have been that wrong about how the universe works? Until finally I got it—and once you get it, it’s hard even to recapture your original confusion, because it becomes so obvious that the world is made not of stuff but of copyable bits of information. “Information wants to be free!”

The No-Cloning Theorem represents nothing less than a partial return to the view of the world that I had before I was five. It says that quantum information doesn’t want to be free: it wants to be private. There is, it turns out, a kind of information that’s tied to a particular place, or set of places. It can be moved around, or even teleported, but it can’t be copied the way a fax machine copies bits.

So I think it’s worth at least entertaining the possibility that we don’t have No-Cloning because of quantum mechanics; we have quantum mechanics because of No-Cloning—or because quantum mechanics is the simplest, most elegant theory that has unclonability as a core principle. But if so, that just pushes the question back to: why should unclonability be a core principle of physics?

Quantum Key Distribution

A first suggestion about this question came from Gilles Brassard, who’s here. Years ago, I attended a talk by Gilles in which he speculated that the laws of quantum mechanics are what they are because Quantum Key Distribution (QKD) has to be possible, while bit commitment has to be impossible. If true, that would be awesome for the people at this conference. It would mean that, far from being this exotic competitor to RSA and Diffie-Hellman that’s distance-limited and bandwidth-limited and has a tiny market share right now, QKD would be the entire reason why the universe is as it is! Or maybe what this really amounts to is an appeal to the Anthropic Principle. Like, if QKD hadn’t been possible, then we wouldn’t be here at QCRYPT to talk about it.

Quantum Money

But maybe we should search more broadly for the reasons why our laws of physics satisfy a No-Cloning Theorem. Wiesner’s paper sort of hinted at QKD, but the main thing it had was a scheme for unforgeable quantum money. This is one of the most direct uses imaginable for the No-Cloning Theorem: to store economic value in something that it’s physically impossible to copy. So maybe that’s the reason for No-Cloning: because God wanted us to have e-commerce, and didn’t want us to have to bother with blockchains (and certainly not with credit card numbers).

The central difficulty with quantum money is: how do you authenticate a bill as genuine? (OK, fine, there’s also the dificulty of how to keep a bill coherent in your wallet for more than a microsecond or whatever. But we’ll leave that for the engineers.)

In Wiesner’s original scheme, he solved the authentication problem by saying that, whenever you want to verify a quantum bill, you bring it back to the bank that printed it. The bank then looks up the bill’s classical serial number in a giant database, which tells the bank in which basis to measure each of the bill’s qubits.

With this system, you can actually get information-theoretic security against counterfeiting. OK, but the fact that you have to bring a bill to the bank to be verified negates much of the advantage of quantum money in the first place. If you’re going to keep involving a bank, then why not just use a credit card?

That’s why over the past decade, some of us have been working on public-key quantum money: that is, quantum money that anyone can verify. For this kind of quantum money, it’s easy to see that the No-Cloning Theorem is no longer enough: you also need some cryptographic assumption. But OK, we can consider that. In recent years, we’ve achieved glory by proposing a huge variety of public-key quantum money schemes—and we’ve achieved even greater glory by breaking almost all of them!

After a while, there were basically two schemes left standing: one based on knot theory by Ed Farhi, Peter Shor, et al. That one has been proven to be secure under the assumption that it can’t be broken. The second scheme, which Paul Christiano and I proposed in 2012, is based on hidden subspaces encoded by multivariate polynomials. For our scheme, Paul and I were able to do better than Farhi et al.: we gave a security reduction. That is, we proved that our quantum money scheme is secure, unless there’s a polynomial-time quantum algorithm to find hidden subspaces encoded by low-degree multivariate polynomials (yadda yadda, you can look up the details) with much greater success probability than we thought possible.

Today, the situation is that my and Paul’s security proof remains completely valid, but meanwhile, our money is completely insecure! Our reduction means the opposite of what we thought it did. There is a break of our quantum money scheme, and as a consequence, there’s also a quantum algorithm to find large subspaces hidden by low-degree polynomials with much better success probability than we’d thought. What happened was that first, some French algebraic cryptanalysts—Faugere, Pena, I can’t pronounce their names—used Gröbner bases to break the noiseless version of scheme, in classical polynomial time. So I thought, phew! At least I had acceded when Paul insisted that we also include a noisy version of the scheme. But later, Paul noticed that there’s a quantum reduction from the problem of breaking our noisy scheme to the problem of breaking the noiseless one, so the former is broken as well.

I’m choosing to spin this positively: “we used quantum money to discover a striking new quantum algorithm for finding subspaces hidden by low-degree polynomials. Err, yes, that’s exactly what we did.”

But, bottom line, until we manage to invent a better public-key quantum money scheme, or otherwise sort this out, I don’t think we’re entitled to claim that God put unclonability into our universe in order for quantum money to be possible.

Copy-Protected Quantum Software

So if not money, then what about its cousin, copy-protected software—could that be why No-Cloning holds? By copy-protected quantum software, I just mean a quantum state that, if you feed it into your quantum computer, lets you evaluate some Boolean function on any input of your choice, but that doesn’t let you efficiently prepare more states that let the same function be evaluated. I think this is important as one of the preeminent evil applications of quantum information. Why should nuclear physicists and genetic engineers get a monopoly on the evil stuff?

OK, but is copy-protected quantum software even possible? The first worry you might have is that, yeah, maybe it’s possible, but then every time you wanted to run the quantum program, you’d have to make a measurement that destroyed it. So then you’d have to go back and buy a new copy of the program for the next run, and so on. Of course, to the software company, this would presumably be a feature rather than a bug!

But as it turns out, there’s a fact many of you know—sometimes called the “Gentle Measurement Lemma,” other times the “Almost As Good As New Lemma”—which says that, as long as the outcome of your measurement on a quantum state could be predicted almost with certainty given knowledge of the state, the measurement can be implemented in such a way that it hardly damages the state at all. This tells us that, if quantum money, copy-protected quantum software, and the other things we’re talking about are possible at all, then they can also be made reusable. I summarize the principle as: “if rockets, then space shuttles.”

Much like with quantum money, one can show that, relative to a suitable oracle, it’s possible to quantumly copy-protect any efficiently computable function—or rather, any function that’s hard to learn from its input/output behavior. Indeed, the implementation can be not only copy-protected but also obfuscated, so that the user learns nothing besides the input/output behavior. As Bill Fefferman pointed out in his talk this morning, the No-Cloning Theorem lets us bypass Barak et al.’s famous result on the impossibility of obfuscation, because their impossibility proof assumed the ability to copy the obfuscated program.

Of course, what we really care about is whether quantum copy-protection is possible in the real world, with no oracle. I was able to give candidate implementations of quantum copy-protection for extremely special functions, like one that just checks the validity of a password. In the general case—that is, for arbitrary programs—Paul Christiano has a beautiful proposal for how to do it, which builds on our hidden-subspace money scheme. Unfortunately, since our money scheme is currently in the shop being repaired, it’s probably premature to think about the security of the much more complicated copy-protection scheme! But these are wonderful open problems, and I encourage any of you to come and scoop us. Once we know whether uncopyable quantum software is possible at all, we could then debate whether it’s the “reason” for our universe to have unclonability as a core principle.

Along the same lines, I can’t resist mentioning some favorite research directions, which some enterprising student here could totally turn into a talk at next year’s QCRYPT.

Firstly, what can we say about clonable versus unclonable quantum proofs—that is, QMA witness states? In other words: for which problems in QMA can we ensure that there’s an accepting witness that lets you efficiently create as many additional accepting witnesses as you want? (I mean, besides the QCMA problems, the ones that have short classical witnesses?) For which problems in QMA can we ensure that there’s an accepting witness that doesn’t let you efficiently create any additional accepting witnesses? I do have a few observations about these questions—ask me if you’re interested—but on the whole, I believe almost anything one can ask about them remains open.

Admittedly, it’s not clear how much use an unclonable proof would be. Like, imagine a quantum state that encoded a proof of the Riemann Hypothesis, and which you would keep in your bedroom, in a glass orb on your nightstand or something. And whenever you felt your doubts about the Riemann Hypothesis resurfacing, you’d take the state out of its orb and measure it again to reassure yourself of RH’s truth. You’d be like, “my preciousssss!” And no one else could copy your state and thereby gain the same Riemann-faith-restoring powers that you had. I dunno, I probably won’t hawk this application in a DARPA grant.

Similarly, one can ask about clonable versus unclonable quantum advice states—that is, initial states that are given to you to boost your computational power beyond that of an ordinary quantum computer. And that’s also a fascinating open problem.

OK, but maybe none of this quite gets at why our universe has unclonability. And this is an after-dinner talk, so do you want me to get to the really crazy stuff? Yes?

OK! What if unclonability is our universe’s way around the paradoxes of self-reference, like the unsolvability of the halting problem and Gödel’s Incompleteness Theorem? Allow me to explain what I mean.

In kindergarten or wherever, we all learn Turing’s proof that there’s no computer program to solve the halting problem. But what isn’t usually stressed is that that proof actually does more than advertised. If someone hands you a program that they claim solves the halting problem, Turing doesn’t merely tell you that that person is wrong—rather, he shows you exactly how to expose the person as a jackass, by constructing an example input on which their program fails. All you do is, you take their claimed halt-decider, modify it in some simple way, and then feed the result back to the halt-decider as input. You thereby create a situation where, if your program halts given its own code as input, then it must run forever, and if it runs forever then it halts. “WHOOOOSH!” [head-exploding gesture]

OK, but now imagine that the program someone hands you, which they claim solves the halting problem, is a quantum program. That is, it’s a quantum state, which you measure in some basis depending on the program you’re interested in, in order to decide whether that program halts. Well, the truth is, this quantum program still can’t work to solve the halting problem. After all, there’s some classical program that simulates the quantum one, albeit less efficiently, and we already know that the classical program can’t work.

But now consider the question: how would you actually produce an example input on which this quantum program failed to solve the halting problem? Like, suppose the program worked on every input you tried. Then ultimately, to produce a counterexample, you might need to follow Turing’s proof and make a copy of the claimed quantum halt-decider. But then, of course, you’d run up against the No-Cloning Theorem!

So we seem to arrive at the conclusion that, while of course there’s no quantum program to solve the halting problem, there might be a quantum program for which no one could explicitly refute that it solved the halting problem, by giving a counterexample.

I was pretty excited about this observation for a day or two, until I noticed the following. Let’s suppose your quantum program that allegedly solves the halting problem has n qubits. Then it’s possible to prove that the program can’t possibly be used to compute more than, say, 2n bits of Chaitin’s constant Ω, which is the probability that a random program halts. OK, but if we had an actual oracle for the halting problem, we could use it to compute as many bits of Ω as we wanted. So, suppose I treated my quantum program as if it were an oracle for the halting problem, and I used it to compute the first 2n bits of Ω. Then I would know that, assuming the truth of quantum mechanics, the program must have made a mistake somewhere. There would still be something weird, which is that I wouldn’t know on which input my program had made an error—I would just know that it must’ve erred somewhere! With a bit of cleverness, one can narrow things down to two inputs, such that the quantum halt-decider must have erred on at least one of them. But I don’t know whether it’s possible to go further, and concentrate the wrongness on a single query.

We can play a similar game with other famous applications of self-reference. For example, suppose we use a quantum state to encode a system of axioms. Then that system of axioms will still be subject to Gödel’s Incompleteness Theorem (which I guess I believe despite the umlaut). If it’s consistent, it won’t be able to prove all the true statements of arithmetic. But we might never be able to produce an explicit example of a true statement that the axioms don’t prove. To do so we’d have to clone the state encoding the axioms and thereby violate No-Cloning.

Personal Identity

But since I’m a bit drunk, I should confess that all this stuff about Gödel and self-reference is just a warmup to what I really wanted to talk about, which is whether the No-Cloning Theorem might have anything to do with the mysteries of personal identity and “free will.” I first encountered this idea in Roger Penrose’s book, The Emperor’s New Mind. But I want to stress that I’m not talking here about the possibility that the brain is a quantum computer—much less about the possibility that it’s a quantum-gravitational hypercomputer that uses microtubules to solve the halting problem! I might be drunk, but I’m not that drunk. I also think that the Penrose-Lucas argument, based on Gödel’s Theorem, for why the brain has to work that way is fundamentally flawed.

But here I’m talking about something different. See, I have a lot of friends in the Singularity / Friendly AI movement. And I talk to them whenever I pass through the Bay Area, which is where they congregate. And many of them express great confidence that before too long—maybe in 20 or 30 years, maybe in 100 years—we’ll be able to upload ourselves to computers and live forever on the Internet (as opposed to just living 70% of our lives on the Internet, like we do today).

This would have lots of advantages. For example, any time you were about to do something dangerous, you’d just make a backup copy of yourself first. If you were struggling with a conference deadline, you’d spawn 100 temporary copies of yourself. If you wanted to visit Mars or Jupiter, you’d just email yourself there. If Trump became president, you’d not run yourself for 8 years (or maybe 80 or 800 years). And so on.

Admittedly, some awkward questions arise. For example, let’s say the hardware runs three copies of your code and takes a majority vote, just for error-correcting purposes. Does that bring three copies of you into existence, or only one copy? Or let’s say your code is run homomorphically encrypted, with the only decryption key stored in another galaxy. Does that count? Or you email yourself to Mars. If you want to make sure that you’ll wake up on Mars, is it important that you delete the copy of your code that remains on earth? Does it matter whether anyone runs the code or not? And what exactly counts as “running” it? Or my favorite one: could someone threaten you by saying, “look, I have a copy of your code, and if you don’t do what I say, I’m going to make a thousand copies of it and subject them all to horrible tortures?”

The issue, in all these cases, is that in a world where there could be millions of copies of your code running on different substrates in different locations—or things where it’s not even clear whether they count as a copy or not—we don’t have a principled way to take as input a description of the state of the universe, and then identify where in the universe you are—or even a probability distribution over places where you could be. And yet you seem to need such a way in order to make predictions and decisions.

A few years ago, I wrote this gigantic, post-tenure essay called The Ghost in the Quantum Turing Machine, where I tried to make the point that we don’t know at what level of granularity a brain would need to be simulated in order to duplicate someone’s subjective identity. Maybe you’d only need to go down to the level of neurons and synapses. But if you needed to go all the way down to the molecular level, then the No-Cloning Theorem would immediately throw a wrench into most of the paradoxes of personal identity that we discussed earlier.

For it would mean that there were some microscopic yet essential details about each of us that were fundamentally uncopyable, localized to a particular part of space. We would all, in effect, be quantumly copy-protected software. Each of us would have a core of unpredictability—not merely probabilistic unpredictability, like that of a quantum random number generator, but genuine unpredictability—that an external model of us would fail to capture completely. Of course, by having futuristic nanorobots scan our brains and so forth, it would be possible in principle to make extremely realistic copies of us. But those copies necessarily wouldn’t capture quite everything. And, one can speculate, maybe not enough for your subjective experience to “transfer over.”

Maybe the most striking aspect of this picture is that sure, you could teleport yourself to Mars—but to do so you’d need to use quantum teleportation, and as we all know, quantum teleportation necessarily destroys the original copy of the teleported state. So we’d avert this metaphysical crisis about what to do with the copy that remained on Earth.

Look—I don’t know if any of you are like me, and have ever gotten depressed by reflecting that all of your life experiences, all your joys and sorrows and loves and losses, every itch and flick of your finger, could in principle be encoded by a huge but finite string of bits, and therefore by a single positive integer. (Really? No one else gets depressed about that?) It’s kind of like: given that this integer has existed since before there was a universe, and will continue to exist after the universe has degenerated into a thin gruel of radiation, what’s the point of even going through the motions? You know?

But the No-Cloning Theorem raises the possibility that at least this integer is really your integer. At least it’s something that no one else knows, and no one else could know in principle, even with futuristic brain-scanning technology: you’ll always be able to surprise the world with a new digit. I don’t know if that’s true or not, but if it were true, then it seems like the sort of thing that would be worthy of elevating unclonability to a fundamental principle of the universe.

So as you enjoy your dinner and dessert at this historic Mayflower Hotel, I ask you to reflect on the following. People can photograph this event, they can video it, they can type up transcripts, in principle they could even record everything that happens down to the millimeter level, and post it on the Internet for posterity. But they’re not gonna get the quantum states. There’s something about this evening, like about every evening, that will vanish forever, so please savor it while it lasts. Thank you.

Update (Sep. 20): Unbeknownst to me, Marc Kaplan did video the event and put it up on YouTube! Click here to watch. Thanks very much to Marc! I hope you enjoy, even though of course, the video can’t precisely clone the experience of having been there.

[Note: The part where I raise my middle finger is an inside joke—one of the speakers during the technical sessions inadvertently did the same while making a point, causing great mirth in the audience.]