The easiest exercise in the moral philosophy book

April 25th, 2021

Peter Singer, in the parable that came to represent his whole worldview and that of the effective altruism movement more generally, asked us to imagine that we could save a drowning child at the cost of jumping into a lake and ruining an expensive new suit. Assuming we’d do that, he argued that we do in fact face an ethically equivalent choice; if we don’t donate most of our income to save children in the Third World, then we need to answer for why, as surely as the person who walked past the kid thrashing in the water.

In this post, I don’t want to take a position on Singer’s difficult but important hypothetical. I merely want to say: suppose that to save the child, you didn’t even have to jump in the water. Suppose you just had to toss a life preserver, one you weren’t using. Or suppose you just had to assure the child that it was OK to grab your life raft that was already in the water.

That, it seems, is the situation that the US and other rich countries will increasingly face with covid vaccines. What’s happening in India right now looks on track to become a humanitarian tragedy, if it isn’t already. Even if, as Indian friends tell me, this was a staggering failure of the Modi government, people shouldn’t pay for it with their lives. And we in the US now have tens of millions of vaccine doses sitting in warehouses unused, for regulatory and vaccine hesitancy reasons—stupidly, but we do. We’re past the time, in my opinion, when it’s morally obligatory either to use the doses or to give them away. Anyone in a position to manufacture more vaccines for distribution to poor countries, should also immediately get the intellectual property rights to do so.

I was glad to read, just this weekend, that the US is finally starting to move in the right direction. I hope it moves faster.

And I’m sorry that this brief post doesn’t contain any information or insight that you can’t find elsewhere. It just made me feel better to write it, is all.

Doubts about teapot supremacy: my reply to Richard Borcherds

April 20th, 2021

Richard Borcherds is a British mathematician at Berkeley, who won the 1998 Fields Medal for the proof of the monstrous moonshine conjecture among many other contributions. A couple months ago, Borcherds posted on YouTube a self-described “rant” about quantum computing, which was recently making the rounds on Facebook and which I found highly entertaining.

Borcherds points out that the term “quantum supremacy” means only that quantum computers can outperform existing classical computers on some benchmark, which can be chosen to show maximum advantage for the quantum computer. He allows that BosonSampling could have some value, for example in calibrating quantum computers or in comparing one quantum computer to another, but he decries the popular conflation of quantum supremacy with the actual construction of a scalable quantum computer able (for example) to run Shor’s algorithm to break RSA.

Borcherds also proposes a “teapot test,” according to which any claim about quantum computers can be dismissed if an analogous claim would hold for a teapot (which he brandishes for the camera). For example, there are many claims to solve practical optimization and machine learning problems by “quantum/classical hybrid algorithms,” wherein a classical computer does most of the work but a quantum computer is somehow involved. Borcherds points out that, at least as things stand in early 2021, in most or all such cases, the classical computer could’ve probably done as well entirely on its own. So then if you put a teapot on top of your classical computer while it ran, you could equally say you used a “classical/teapot hybrid approach.”

Needless to say, Borcherds is correct about all of this. I’ve made similar points on this blog for 15 years, although less Britishly. I’m delighted to have such serious new firepower on the scoffing-at-QC-hype team.

I do, however, have one substantive disagreement. At one point, Borcherds argues that sampling-based quantum supremacy itself fails his teapot test. For consider the computational problem of predicting how many pieces a teapot will break into if it’s dropped on the ground. Clearly, he says, the teapot itself will outperform any simulation running on any existing classical computer at that task, and will therefore achieve “teapot supremacy.” But who cares??

I’m glad that Borcherds has set out, rather crisply, an objection that’s been put to me many times over the past decade. The response is simple: I don’t believe the teapot really does achieve teapot supremacy on the stated task! At the least, I’d need to be shown why. You can’t just assert it without serious argument.

If we want to mirror the existing quantum supremacy experiments, then the teapot computational problem, properly formulated, should be: given as input a description of a teapot’s construction, the height from which it’s dropped, etc., output a sample from the probability distribution over the number of shards that the teapot will break into when it hits the floor.

If so, though, then clearly a classical computer can easily sample from the same distribution! Why? Because presumably we agree that there’s a negligible probability of more than (say) 1000 shards. So the distribution is characterized by a list of at most 1000 probabilities, which can be estimated empirically (at the cost of a small warehouse of smashed teapots) and thereafter used to generate samples. In the plausible event that the distribution is (say) a Gaussian, it’s even easier: just estimate the mean and variance.

A couple days ago, I was curious what the distribution looked like, so I decided to order some teapots from Amazon and check. Unfortunately, real porcelain teapots are expensive, and it seemed vaguely horrific to order dozens (as would be needed to get reasonable data) for the sole purpose of smashing them on my driveway. So I hit on what seemed like a perfect solution: I ordered toy teapots, which were much smaller and cheaper. Alas, when my toy “porcelain” teapots arrived yesterday, they turned out (unsurprisingly in retrospect for a children’s toy) to be some sort of plastic or composite material, meaning that they didn’t break unless one propelled them downward forcefully. So, while I can report that they tended to break into one or two large pieces along with two or three smaller shards, I found it impossible to get better data. (There’s a reason why I became a theoretical computer scientist…)

The good news is that my 4-year-old son had an absolute blast smashing toy teapots with me on our driveway, while my 8-year-old daughter was thrilled to take the remaining, unbroken teapots for her dollhouse. I apologize if this fails to defy gender stereotypes.

Anyway, it might be retorted that it’s not good enough to sample from a probability distribution: what’s wanted, rather, is to calculate how many pieces this specific teapot will break into, given all the microscopic details of it and its environment. Aha, this brings us to a crucial conceptual point: in order for something to count as an “input” to a computer, you need to be able to set it freely. Certainly, at the least, you need to be able to measure and record the input in its entirety, so that someone trying to reproduce your computation on a standard silicon computer would know exactly which computation to do. You don’t get to claim computational supremacy based on a problem with secret inputs: that’s like failing someone on a math test without having fully told them the problems.

Ability to set and know the inputs is the key property that’s satisfied by Google’s quantum supremacy experiment, and to a lesser extent by the USTC BosonSampling experiment, but that’s not satisfied at all by the “smash a teapot on the floor” experiment. Or perhaps it’s better to say: influences on a computation that vary uncontrollably and chaotically, like gusts of air hitting the teapot as it falls to the floor, shouldn’t be called “inputs” at all; they’re simply noise sources. And what one does with noise sources is to try to estimate their distribution and average over them—but in that case, as I said, there’s no teapot supremacy.

A Facebook friend said to me: that’s well and good, but surely we could change Borcherds’s teapot experiment to address this worry? For example: add a computer-controlled lathe (or even a 3D printer), with which you can build a teapot in an arbitrary shape of your choice. Then consider the problem of sampling from the probability distribution over how many pieces that teapot will smash into, when it’s dropped from some standard height onto some standard surface. I replied that this is indeed more interesting—in fact, it already seems more like what engineers do in practice (still, sometimes!) when building wind tunnels, than like a silly reductio ad absurdum of quantum supremacy experiments. On the other hand, if you believe the Extended Church-Turing Thesis, then as long as your analog computer is governed by classical physics, it’s presumably inherently limited to an Avogadro’s number type speedup over a standard digital computer, whereas with a quantum computer, you’re limited only by the exponential dimensionality of Hilbert space, which seems more interesting.

Or maybe I’m wrong—in which case, I look forward to the first practical demonstration of teapot supremacy! Just like with quantum supremacy, though, it’s not enough to assert it; you need to … put the tea where your mouth is.

Update: On the suggestion of Ernest Davis, who I can now reveal as the Facebook friend mentioned above, I just ordered some terra cotta flower pots, which look cheap, easily smashable, and environmentally friendly, and which will hopefully be acceptable substitutes for porcelain teapots in a new experiment. (Not that my main arguments in this post hinge on the results of such an experiment! That’s the power of theory.)

Another Update: Some of you might enjoy John Horgan’s Scientific American column on reality vs. hype in quantum computing, based on conversations with me and with Terry Rudolph of PsiQuantum.

The ACM Prize thing

April 14th, 2021

Last week I got an email from Dina Katabi, my former MIT colleague, asking me to call her urgently. Am I in trouble? For what, though?? I haven’t even worked at MIT for five years!

Luckily, Dina only wanted to tell me that I’d been selected to receive the 2020 ACM Prize in Computing, a mid-career award founded in 2007 that comes with $250,000 from Infosys. Not the Turing Award but I’d happily take it! And I could even look back on 2020 fondly for something.

I was utterly humbled to see the list of past ACM Prize recipients, which includes amazing computer scientists I’ve been privileged to know and learn from (like Jon Kleinberg, Sanjeev Arora, and Dan Boneh) and others who I’ve admired from afar (like Daphne Koller, Jeff Dean and Sanjay Ghemawat of Google MapReduce, and David Silver of AlphaGo and AlphaZero).

I was even more humbled, later, to read my prize citation, which focuses on four things:

  1. The theoretical foundations of the sampling-based quantum supremacy experiments now being carried out (and in particular, my and Alex Arkhipov’s 2011 paper on BosonSampling);
  2. My and Avi Wigderson’s 2008 paper on the algebrization barrier in complexity theory;
  3. Work on the limitations of quantum computers (in particular, the 2002 quantum lower bound for the collision problem); and
  4. Public outreach about quantum computing, including through QCSD, popular talks and articles, and this blog.

I don’t know if I’m worthy of such a prize—but I know that if I am, then it’s mainly for work I did between roughly 2001 and 2012. This honor inspires me to want to be more like I was back then, when I was driven, non-jaded, and obsessed with figuring out the contours of BQP and efficient computation in the physical universe. It makes me want to justify the ACM’s faith in me.

I’m grateful to the committee and nominators, and more broadly, to the whole quantum computing and theoretical computer science communities—which I “joined” in some sense around age 16, and which were the first communities where I ever felt like I belonged. I’m grateful to the mentors who made me what I am, especially Chris Lynch, Bart Selman, Lov Grover, Umesh Vazirani, Avi Wigderson, and (if he’ll allow me to include him) John Preskill. I’m grateful to the slightly older quantum computer scientists who I looked up to and tried to emulate, like Dorit Aharonov, Andris Ambainis, Ronald de Wolf, and John Watrous. I’m grateful to my wonderful colleagues at UT Austin, in the CS department and beyond. I’m grateful to my students and postdocs, the pride of my professional life. I’m grateful, of course, to my wife, parents, and kids.

By coincidence, my last post was also about prizes to theoretical computer scientists—in that case, two prizes that attracted controversy because of the recipient’s (or would-be recipient’s) political actions or views. It would understate matters to point out that not everyone has always agreed with everything I’ve said on this blog. I’m ridiculously lucky, and I know it, that even living through this polarized and tumultuous era, I never felt forced to choose between academic success and the freedom to speak my conscience in public under my real name. If there’s been one constant in my public stands, I’d like to think that—inspired by memories of my own years as an unknown, awkward, self-conscious teenager—it’s been my determination to nurture and protect talented young scientists, whatever they look like and wherever they come from. And I’ve tried to live up to that ideal in real life, and I welcome anyone’s scrutiny as to how well I’ve done.

What should I do with the prize money? I confess that my first instinct was to donate it, in its entirety, to some suitable charity—specifically, something that would make all the strangers who’ve attacked me on Twitter, Reddit, and so forth over the years realize that I’m fundamentally a good person. But I was talked out of this plan by my family, who pointed out that
(1) in all likelihood, nothing will make online strangers stop hating me,
(2) in any case this seems like a poor basis for making decisions, and
(3) if I really want to give others a say in what to do with the winnings, then why not everyone who’s stood by me and supported me?

So, beloved commenters! Please mention your favorite charitable causes below, especially weird ones that I wouldn’t have heard of otherwise. If I support their values, I’ll make a small donation from my prize winnings. Or a larger donation, especially if you donate yourself and challenge me to match. Whatever’s left after I get tired of donating will probably go to my kids’ college fund.

Update: And by an amusing coincidence, today is apparently “World Quantum Day”! I hope your Quantum Day is as pleasant as mine (and stable and coherent).

Just some prizes

April 9th, 2021

Oded Goldreich is a theoretical computer scientist at the Weizmann Institute in Rehovot, Israel. He’s best known for helping to lay the rigorous foundations of cryptography in the 1980s, through seminal results like the Goldreich-Levin Theorem (every one-way function can be modified to have a hard-core predicate), the Goldreich-Goldwasser-Micali Theorem (every pseudorandom generator can be made into a pseudorandom function), and the Goldreich-Micali-Wigderson protocol for secure multi-party computation. I first met Oded more than 20 years ago, when he lectured at a summer school at the Institute for Advanced Study in Princeton, barefoot and wearing a tank top and what looked like pajama pants. It was a bracing introduction to complexity-theoretic cryptography. Since then, I’ve interacted with Oded from time to time, partly around his firm belief that quantum computing is impossible.

Last month a committee in Israel voted to award Goldreich the Israel Prize (roughly analogous to the US National Medal of Science), for which I’d say Goldreich had been a plausible candidate for decades. But alas, Yoav Gallant, Netanyahu’s Education Minister, then rather non-gallantly blocked the award, solely because he objected to Goldreich’s far-left political views (and apparently because of various statements Goldreich signed, including in support of a boycott of Ariel University, which is in the West Bank). The case went all the way to the Israeli Supreme Court (!), which ruled two days ago in Gallant’s favor: he gets to “delay” the award to investigate the matter further, and in the meantime has apparently sent out invitations for an award ceremony next week that doesn’t include Goldreich. Some are now calling for the other winners to boycott the prize in solidarity until this is righted.

I doubt readers of this blog need convincing that this is a travesty and an embarrassment, a shanda, for the Netanyahu government itself. That I disagree with Goldreich’s far-left views (or might disagree, if I knew in any detail what they were) is totally immaterial to that judgment. In my opinion, not even Goldreich’s belief in the impossibility of quantum computers should affect his eligibility for the prize. 🙂

Maybe it would be better to say that, as far as his academic colleagues in Israel and beyond are concerned, Goldreich has won the Israel Prize; it’s only some irrelevant external agent who’s blocking his receipt of it. Ironically, though, among Goldreich’s many heterodox beliefs is a total rejection of the value of scientific prizes (although Goldreich has also said he wouldn’t refuse the Israel Prize if offered it!).

In unrelated news, the 2020 Turing Award has been given to Al Aho and Jeff Ullman. Aho and Ullman have both been celebrated leaders in CS for half a century, having laid many of the foundations of formal languages and compilers, and having coauthored one of CS’s defining textbooks with John Hopcroft (who already received a different Turing Award).

But again there’s a controversy. Apparently, in 2011, Ullman wrote to an Iranian student who wanted to work with him, saying that as “a matter of principle,” he would not accept Iranian students until the Iranian government recognized Israel. Maybe I should say that I, like Ullman, am both a Jew and a Zionist, but I find it hard to imagine the state of mind that would cause me to hold some hapless student responsible for the misdeeds of their birth-country’s government. Ironically, this is a mirror-image of the tactics that the BDS movement has wielded against Israeli academics. Unlike Goldreich, though, Ullman seems to have gone beyond merely expressing his beliefs, actually turning them into a one-man foreign policy.

I’m proud of the Iranian students I’ve mentored and hope to mentor more. While I don’t think this issue should affect Ullman’s Turing Award (and I haven’t seen anyone claim that it should), I do think it’s appropriate to use the occasion to express our opposition to all forms of discrimination. I fully endorse Shafi Goldwasser’s response in her capacity as Director of the Simons Institute for Theory of Computing in Berkeley:

As a senior member of the computer science community and an American-Israeli, I stand with our Iranian students and scholars and outright reject any notion by which admission, support, or promotion of individuals in academic settings should be impeded by national origin or politics. Individuals should not be conflated with the countries or institutions they come from. Statements and actions to the contrary have no place in our computer science community. Anyone experiencing such behavior will find a committed ally in me.

As for Al Aho? I knew him fifteen years ago, when he became interested in quantum computing, in part due to his then-student Krysta Svore (who’s now the head of Microsoft’s quantum computing efforts). Al struck me as not only a famous scientist but a gentleman who radiated kindness everywhere. I’m not aware of any controversies he’s been involved in and never heard anyone say a bad word about him.

Anyway, this seems like a good occasion to recognize some foundational achievements in computer science, as well as the complex human beings who produce them!

The Computational Expressiveness of a Model Train Set: A Paperlet

April 4th, 2021

Update (April 5, 2021): So it turns out that Adam Chalcraft and Michael Greene already proved the essential result of this post back in 1994 (hat tip to commenter Dylan). Not terribly surprising in retrospect!

My son Daniel had his fourth birthday a couple weeks ago. For a present, he got an electric train set. (For completeness—and since the details of the train set will be rather important to the post—it’s called “WESPREX Create a Dinosaur Track”, but this is not an ad and I’m not getting a kickback for it.)

As you can see, the main feature of this set is a Y-shaped junction, which has a flap that can control which direction the train goes. The logic is as follows:

  • If the train is coming up from the “bottom” of the Y, then it continues to either the left arm or the right arm, depending on where the flap is. It leaves the flap as it was.
  • If the train is coming down the left or right arms of the Y, then it continues to the bottom of the Y, pushing the flap out of its way if it’s in the way. (Thus, if the train were ever to return to this Y-junction coming up from the bottom, not having passed the junction in the interim, it would necessarily go to the same arm, left or right, that it came down from.)

The train set also comes with bridges and tunnels; thus, there’s no restriction of planarity. Finally, the train set comes with little gadgets that can reverse the train’s direction, sending it back in the direction that it came from:

These gadgets don’t seem particularly important, though, since we could always replace them if we wanted by a Y-junction together with a loop.

Notice that, at each Y-junction, the position of the flap stores one bit of internal state, and that the train can both “read” and “write” these bits as it moves around. Thus, a question naturally arises: can this train set do any nontrivial computations? If there are n Y-junctions, then can it cycle through exp(n) different states? Could it even solve PSPACE-complete problems, if we let it run for exponential time? (For a very different example of a model-train-like system that, as it turns out, is able to express PSPACE-complete problems, see this recent paper by Erik Demaine et al.)

Whatever the answers regarding Daniel’s train set, I knew immediately on watching the thing go that I’d have to write a “paperlet” on the problem and publish it on my blog (no, I don’t inflict such things on journals!). Today’s post constitutes my third “paperlet,” on the general theme of a discrete dynamical system that someone showed me in real life (e.g. in a children’s toy or in biology) having more structure and regularity than one might naïvely expect. My first such paperlet, from 2014, was on a 1960s toy called the Digi-Comp II; my second, from 2016, was on DNA strings acted on by recombinase (OK, that one was associated with a paper in Science, but my combinatorial analysis wasn’t the main point of the paper).

Anyway, after spending an enjoyable evening on the problem of Daniel’s train set, I was able to prove that, alas, the possible behaviors are quite limited (I classified them all), falling far short of computational universality.

If you feel like I’m wasting your time with trivialities (or if you simply enjoy puzzles), then before you read any further, I encourage you to stop and try to prove this for yourself!

Back yet? OK then…

Theorem: Assume a finite amount of train track. Then after a linear amount of time, the train will necessarily enter a “boring infinite loop”—i.e., an attractor state in which at most two of the flaps keep getting toggled, and the rest of the flaps are fixed in place. In more detail, the attractor must take one of four forms:

I. a line (with reversing gadgets on both ends),
II. a simple cycle,
III. a “lollipop” (with one reversing gadget and one flap that keeps getting toggled), or
IV. a “dumbbell” (with two flaps that keep getting toggled).

In more detail still, there are seven possible topologically distinct trajectories for the train, as shown in the figure below.

Here the red paths represent the attractors, where the train loops around and around for an unlimited amount of time, while the blue paths represent “runways” where the train spends a limited amount of time on its way into the attractor. Every degree-3 vertex is assumed to have a Y-junction, while every degree-1 vertex is assumed to have a reversing gadget, unless (in IIb) the train starts at that vertex and never returns to it.

The proof of the theorem rests on two simple observations.

Observation 1: While the Y-junctions correspond to vertices of degree 3, there are no vertices of degree 4 or higher. This means that, if the train ever revisits a vertex v (other than the start vertex) for a second time, then there must be some edge e incident to v that it also traverses for a second time immediately afterward.

Observation 2: Suppose the train traverses some edge e, then goes around a simple cycle (meaning, one where no edges or vertices are reused), and then traverses e again, going in the same direction as the first time. Then from that point forward, the train will just continue around the same simple cycle forever.

The proof of Observation 2 is simply that, if there were any flap that might be in the train’s way as it continued around the simple cycle, then the train would already have pushed it out of the way its first time around the cycle, and nothing that happened thereafter could possibly change the flap’s position.

Using the two observations above, let’s now prove the theorem. Let the train start where it will, and follow it as it traces out a path. Since the graph is finite, at some point some already-traversed edge must be traversed a second time. Let e be the first such edge. By Observation 1, this will also be the first time the train’s path intersects itself at all. There are then three cases:

Case 1: The train traverses e in the same direction as it did the first time. By Observation 2, the train is now stuck in a simple cycle forever after. So the only question is what the train could’ve done before entering the simple cycle. We claim that at most, it could’ve traversed a simple path. For otherwise, we’d contradict the assumption that e was the first edge that the train visited twice on its journey. So the trajectory must have type IIa, IIb, or IIc in the figure.

Case 2: Immediately after traversing e, the train hits a reversing gadget and traverses e again the other way. In this case, the train will clearly retrace its entire path and then continue past its starting point; the question is what happens next. If it hits another reversing gadget, then the trajectory will have type I in the figure. If it enters a simple cycle and stays in it, then the trajectory will have type IIb in the figure. If, finally, it makes a simple cycle and then exits the cycle, then the trajectory will have type III in the figure. In this last case, the train’s trajectory will form a “lollipop” shape. Note that there must be a Y-junction where the “stick” of the lollipop meets the “candy” (i.e., the simple cycle), with the base of the Y aligned with the stick (since otherwise the train would’ve continued around and around the candy). From this, we deduce that every time the train goes around the candy, it does so in a different orientation (clockwise or counterclockwise) than the time before; and that the train toggles the Y-junction’s flap every time it exits the candy (although not when it enters the candy).

Case 3: At some point after traversing e in the forward direction (but not immediately after), the train traverses e in the reverse direction. In this case, the broad picture is analogous to Case 2. So far, the train has made a lollipop with a Y-junction connecting the stick to the candy (i.e. cycle), the base of the Y aligned with the stick, and e at the very top of the stick. The question is what happens next. If the train next hits a reversing gadget, the trajectory will have type III in the figure. If it enters a new simple cycle, disjoint from the first cycle, and never leaves it, the trajectory will have type IId in the figure. If it enters a new simple cycle, disjoint from the first cycle, and does leave it, then the trajectory now has a “dumbbell” pattern, type IV in the figure (also shown in the first video). There’s only one other situation to worry about: namely, that the train makes a new cycle that intersects the first cycle, forming a “theta” (θ) shaped trajectory. In this case, there must be a Y-junction at the point where the new cycle bumps into the old cycle. Now, if the base of the Y isn’t part of the old cycle, then the train never could’ve made it all the way around the old cycle in the first place (it would’ve exited the old cycle at this Y-junction), contradiction. If the base of the Y is part of the old cycle, then the flap must have been initially set to let the train make it all the way around the old cycle; when the train then reenters the old cycle, the flap must be moved so that the train will never make it all the way around the old cycle again. So now the train is stuck in a new simple cycle (sharing some edges with the old cycle), and the trajectory has type IIc in the figure.

This completes the proof of the theorem.

We might wonder: why isn’t this model train set capable of universal computation, of AND, OR, and NOT gates—or at any rate, of some computation more interesting than repeatedly toggling one or two flaps? My answer might sound tautological: it’s simply that the logic of the Y-junctions is too limited. Yes, the flaps can get pushed out of the way—that’s a “bit flip”—but every time such a flip happens, it helps to set up a “groove” in which the train just wants to continue around and around forever, not flipping any additional bits, with only the minor complications of the lollipop and dumbbell structures to deal with. Even though my proof of the theorem might’ve seemed like a tedious case analysis, it had this as its unifying message.

It’s interesting to think about what gadgets would need to be added to the train set to make it computationally universal, or at least expressively richer—able, as turned out to be the case for the Digi-Comp II, to express some nontrivial complexity class falling short of P. So for example, what if we had degree-4 vertices, with little turnstile gadgets? Or multiple trains, which could be synchronized to the millisecond to control how they interacted with each other via the flaps, or which could even crash into each other? I look forward to reading your ideas in the comment section!

For the truth is this: quantum complexity classes, BosonSampling, closed timelike curves, circuit complexity in black holes and AdS/CFT, etc. etc.—all these topics are great, but the same models and problems do get stale after a while. I aspire for my research agenda to chug forward, full steam ahead, into new computational domains.

PS. Happy Easter to those who celebrate!

QC ethics and hype: the call is coming from inside the house

March 20th, 2021

For years, I’d sometimes hear discussions about the ethics of quantum computing research. Quantum ethics!

When the debates weren’t purely semantic, over the propriety of terms like “quantum supremacy” or “ancilla qubit,” they were always about chin-strokers like “but what if cracking RSA encryption gives governments more power to surveil their citizens? or what if only a few big countries or companies get quantum computers, thereby widening the divide between haves and have-nots?” Which, OK, conceivably these will someday be issues. But, besides barely depending on any specific facts about quantum computing, these debates always struck me as oddly safe, because the moral dilemmas were so hypothetical and far removed from us in time.

I confess I may have even occasionally poked fun when asked to expound on quantum ethics. I may have commented that quantum computers probably won’t kill anyone unless a dilution refrigerator tips over onto their head. I may have asked forgiveness for feeding custom-designed oracles to BQP and QMA, without first consulting an ethics committee about the long-term effects on those complexity classes.

Now fate has punished me for my flippancy. These days, I really do feel like quantum computing research has become an ethical minefield—but not for any of the reasons mentioned previously. What’s new is that millions of dollars are now potentially available to quantum computing researchers, along with equity, stock options, and whatever else causes “ka-ching” sound effects and bulging eyes with dollar signs. And in many cases, to have a shot at such riches, all an expert needs to do is profess optimism that quantum computing will have revolutionary, world-changing applications and have them soon. Or at least, not object too strongly when others say that.

Some of today’s rhetoric will of course remind people of the D-Wave saga, which first brought this blog to prominence when it began in earnest in 2007. Quantum computers, we hear now as then, will soon leave the Earth’s fastest supercomputers in the dust. They’re going to harness superposition to try all the exponentially many possible solutions at once. They’ll crack the Traveling Salesman Problem, and will transform machine learning and AI beyond recognition. Meanwhile, simulations of quantum systems will be key to solving global warming and cancer.

Despite the parallels, though, this new gold rush doesn’t feel to me like the D-Wave one, which seems in retrospect like just a little dry run. If I had to articulate what’s new in one sentence, it’s that this time “the call is coming from inside the house.” Many of the companies making wildly overhyped claims are recognized leaders of the field. They have brilliant quantum computing theorists and experimentalists on their staff with impeccable research records. Some of those researchers are among my best friends. And even when I wince at the claims of near-term applications, in many cases (especially with quantum simulation) the claims aren’t obviously false—we won’t know for certain until we try it and see! It’s genuinely gotten harder to draw the line between defensible optimism and exaggerations verging on fraud.

Indeed, this time around virtually everyone in QC is “complicit” to a greater or lesser degree. I, too, have accepted compensation to consult on quantum computing topics, to give talks at hedge funds, and in a few cases to serve as a scientific adviser to quantum computing startups. I tell myself that, by 2021 standards, this stuff is all trivial chump change—a few thousands of dollars here or there, to expound on the same themes that I already discuss free of charge on this blog. I actually get paid to dispel hype, rather than propagate it! I tell myself that I’ve turned my back on the orders of magnitude more money available to those willing to hitch their scientific reputations to the aspirations of this or that specific QC company. (Yes, this blog, and my desire to preserve its intellectual independence and credibility, might well be costing me millions!)

But, OK, some would argue that accepting any money from QC companies or QC investors just puts you at the top of a slope with unabashed snake-oil salesmen at the bottom. With the commercialization of our field that started around 2015, there’s no bright line anymore marking the boundary between pure scientific curiosity and the pursuit of filthy lucre; it’s all just points along a continuum. I’m not sure that these people are wrong.

As some of you might’ve seen already, IonQ, the trapped-ion QC startup that originated from the University of Maryland, is poised to have the first-ever quantum computing IPO—a so-called “SPAC IPO,” which while I’m a financial ignoramus, apparently involves merging with a shell company and thereby bypassing the SEC’s normal IPO rules. Supposedly they’re seeking $650 million in new funding and a $2 billion market cap. If you want to see what IonQ is saying about QC to prospective investors, click here. Lacking any choice in the matter, I’ll probably say more about these developments in a future post.

Meanwhile, PsiQuantum, the Palo-Alto-based optical QC startup, has said that it’s soon going to leave “stealth mode.” And Amazon, Microsoft, Google, IBM, Honeywell, and other big players continue making large investments in QC—treating it, at least rhetorically, not at all like blue-sky basic research, but like a central part of their future business plans.

All of these companies have produced or funded excellent QC research. And of course, they’re all heterogeneous, composed of individuals who might vehemently disagree with each other about the near- or long-term prospects of QC. And yet all of them have, at various times, inspired reflections in me like the ones in this post.

I regret that this post has no clear conclusion. I’m still hashing things out, solicing thoughts from my readers and friends. Speaking of which: this coming Monday, March 22, at 8-10pm US Eastern time, I’ve decided to hold a discussion around these issues on Clubhouse—my “grand debut” on that app, and an opportunity to see whether I like it or not! My friend Adam Brown will moderate the discussion; other likely participants will be John Horgan, George Musser, Michael Nielsen, and Matjaž Leonardis. If you’re on Clubhouse, I hope to see you there!

Update (March 22): Read this comment by “FB” if you’d like to understand how we got to this point.

Abel to win

March 17th, 2021

Many of you will have seen the happy news today that Avi Wigderson and László Lovász share this year’s Abel Prize (which now contends with the Fields Medal for the highest award in pure math). This is only the second time that the Abel Prize has been given wholly or partly for work in theoretical computer science, after Szemerédi in 2012. See also the articles in Quanta or the NYT, which actually say most of what I would’ve said for a lay audience about Wigderson’s and Lovász’s most famous research results and their importance (except, no, Avi hasn’t yet proved P=BPP, just taken some major steps toward it…).

On a personal note, Avi was both my and my wife Dana’s postdoctoral advisor at the Institute for Advanced Study in Princeton. He’s been an unbelievably important mentor to both of us, as he’s been for dozens of others in the CS theory community. Back in 2007, I also had the privilege of working closely with Avi for months on our Algebrization paper. Now would be a fine time to revisit Avi’s Permanent Impact on Me (or watch the YouTube video), which is the talk I gave at IAS in 2016 on the occasion of Avi’s 60th birthday.

Huge congratulations to both Avi and László!

Long-delayed UT Austin Quantum Complexity Theory Student Project Showcase!

March 11th, 2021

Back at MIT, whenever I taught my graduate course on Quantum Complexity Theory (see here for lecture notes), I had a tradition of showcasing the student projects on this blog: see here (Fall 2010), here (Fall 2012), here (Fall 2014). I was incredibly proud that, each time I taught, at least some of the projects led to publishable original research—sometimes highly significant research, like Paul Christiano’s work on quantum money (which led to my later paper with him), Shelby Kimmel’s work on quantum query complexity, Jenny Barry’s work on quantum partially observable Markov decision processes (“QOMDPs”), or Matt Coudron and Henry Yuen’s work on randomness expansion (which led to their later breakthrough in the subject).

Alas, after I moved to UT Austin, for some reason I discontinued the tradition of these blog-showcases—and inexcusably, I did this even though the wonderful new research results continued! Now that I’m teaching Quantum Complexity Theory at UT for the third time (via Zoom, of course), I decided that it was finally time to remedy this. To keep things manageable, this time I’m going to limit myself to research projects that began their lives in my course and that are already public on the arXiv (or in one case, that will soon be).

So please enjoy the following smorgasbord, from 2016 and 2019 iterations of my course! And if you have any questions about any of the projects—well, I’ll try to get the students to answer in the comments section! Thanks so much and congratulations to the students for their work.

From the Fall 2016 iteration of the course

William Hoza (project turned into a joint paper with Cole Graham), Universal Bell Correlations Do Not Exist.

We prove that there is no finite-alphabet nonlocal box that generates exactly those correlations that can be generated using a maximally entangled pair of qubits. More generally, we prove that if some finite-alphabet nonlocal box is strong enough to simulate arbitrary local projective measurements of a maximally entangled pair of qubits, then that nonlocal box cannot itself be simulated using any finite amount of entanglement. We also give a quantitative version of this theorem for approximate simulations, along with a corresponding upper bound.

Patrick Rall, Signed quantum weight enumerators characterize qubit magic state distillation.

Many proposals for fault-tolerant quantum computation require injection of ‘magic states’ to achieve a universal set of operations. Some qubit states are above a threshold fidelity, allowing them to be converted into magic states via ‘magic state distillation’, a process based on stabilizer codes from quantum error correction.
We define quantum weight enumerators that take into account the sign of the stabilizer operators. These enumerators completely describe the magic state distillation behavior when distilling T-type magic states. While it is straightforward to calculate them directly by counting exponentially many operator weights, it is also an NP-hard problem to compute them in general. This suggests that finding a family of distillation schemes with desired threshold properties is at least as hard as finding the weight distributions of a family of classical codes.
Additionally, we develop search algorithms fast enough to analyze all useful 5 qubit codes and some 7 qubit codes, finding no codes that surpass the best known threshold.

From the Spring 2019 iteration of the course

Ying-Hao Chen, 2-Local Hamiltonian with Low Complexity is QCMA-complete.

We prove that 2-Local Hamiltonian (2-LH) with Low Complexity problem is QCMA-complete by combining the results from the QMA-completeness of 2-LH and QCMA-completeness of 3-LH with Low Complexity. The idea is straightforward. It has been known that 2-LH is QMA-complete. By putting a low complexity constraint on the input state, we make the problem QCMA. Finally, we use similar arguments as in [Kempe, Kitaev, Regev] to show that all QCMA problems can be reduced to our proposed problem.

Jeremy Cook, On the relationships between Z-, C-, and H-local unitaries.

Quantum walk algorithms can speed up search of physical regions of space in both the discrete-time [arXiv:quant-ph/0402107] and continuous-time setting [arXiv:quant-ph/0306054], where the physical region of space being searched is modeled as a connected graph. In such a model, Aaronson and Ambainis [arXiv:quant-ph/0303041] provide three different criteria for a unitary matrix to act locally with respect to a graph, called Z-local, C-local, and H-local unitaries, and left the open question of relating these three locality criteria. Using a correspondence between continuous- and discrete-time quantum walks by Childs [arXiv:0810.0312], we provide a way to approximate N×N H-local unitaries with error δ using O(1/√δ,√NC-local unitaries, where the comma denotes the maximum of the two terms.

Joshua A. Cook, Approximating Unitary Preparations of Orthogonal Black Box States.

In this paper, I take a step toward answering the following question: for m different small circuits that compute m orthogonal n qubit states, is there a small circuit that will map m computational basis states to these m states without any input leaving any auxiliary bits changed. While this may seem simple, the constraint that auxiliary bits always be returned to 0 on any input (even ones besides the m we care about) led me to use sophisticated techniques. I give an approximation of such a unitary in the m = 2 case that has size polynomial in the approximation error, and the number of qubits n.

Sabee Grewal (project turned into a joint paper with me), Efficient Learning of Non-Interacting Fermion Distributions.

We give an efficient classical algorithm that recovers the distribution of a non-interacting fermion state over the computational basis. For a system of n non-interacting fermions and m modes, we show that O(m2n4log(m/δ)/ε4) samples and O(m4n4log(m/δ)/ε4) time are sufficient to learn the original distribution to total variation distance ε with probability 1−δ. Our algorithm empirically estimates the one- and two-mode correlations and uses them to reconstruct a succinct description of the entire distribution efficiently.

Sam Gunn and Niels Kornerup, Review of a Quantum Algorithm for Betti Numbers.

We looked into the algorithm for calculating Betti numbers presented by Lloyd, Garnerone, and Zanardi (LGZ). We present a new algorithm in the same spirit as LGZ with the intent of clarifying quantum algorithms for computing Betti numbers. Our algorithm is simpler and slightly more efficient than that presented by LGZ. We present a thorough analysis of our algorithm, pointing out reasons that both our algorithm and that presented by LGZ do not run in polynomial time for most inputs. However, the algorithms do run in polynomial time for calculating an approximation of the Betti number to polynomial multiplicative error, when applied to some class of graphs for which the Betti number is exponentially large.

William Kretschmer, Lower Bounding the AND-OR Tree via Symmetrization.

We prove a simple, nearly tight lower bound on the approximate degree of the two-level AND-OR tree using symmetrization arguments. Specifically, we show that ~deg(ANDm∘ORn)=Ω(~(mn)). To our knowledge, this is the first proof of this fact that relies on symmetrization exclusively; most other proofs involve the more complicated formulation of approximate degree as a linear program [BT13, She13, BDBGK18]. Our proof also demonstrates the power of a symmetrization technique involving Laurent polynomials (polynomials with negative exponents) that was previously introduced by Aaronson, Kothari, Kretschmer, and Thaler [AKKT19].

Jiahui Liu and Ruizhe Zhang (project turned into a joint paper with me, Mark Zhandry, and Qipeng Liu),
New Approaches for Quantum Copy-Protection.

Quantum copy protection uses the unclonability of quantum states to construct quantum software that provably cannot be pirated. Copy protection would be immensely useful, but unfortunately little is known about how to achieve it in general. In this work, we make progress on this goal, by giving the following results:
– We show how to copy protect any program that cannot be learned from its input/output behavior, relative to a classical oracle. This improves on Aaronson [CCC’09], which achieves the same relative to a quantum oracle. By instantiating the oracle with post-quantum candidate obfuscation schemes, we obtain a heuristic construction of copy protection.
– We show, roughly, that any program which can be watermarked can be copy detected, a weaker version of copy protection that does not prevent copying, but guarantees that any copying can be detected. Our scheme relies on the security of the assumed watermarking, plus the assumed existence of public key quantum money. Our construction is general, applicable to many recent watermarking schemes.

John Kallaugher, Triangle Counting in the Quantum Streaming Model. Not yet available but coming soon to an arXiv near you!

We give a quantum algorithm for counting triangles in graph streams that uses less space than the best possible classical algorithm.

Sayonara Majorana?

March 10th, 2021

Many of you have surely already seen the news that the Kouwenhoven group in Delft—which in 2018 published a paper in Nature claiming to have detected Majorana particles, a type of nonabelian anyon—have retracted the paper and apologized for “insufficient scientific rigour.” This work was considered one of the linchpins of Microsoft’s experimental effort toward building topological quantum computers.

Like most quantum computing theorists, I guess, I’m thrilled if Majorana particles can be created using existing technology, I’m sad if they can’t be, but I don’t have any special investment in or knowledge of the topic, beyond what I read in the news or hear from colleagues. Certainly Majorana particles seem neither necessary nor sufficient for building a scalable quantum computer, although they’d be a step forward for the topological approach to QC.

The purpose of this post is to invite informed scientific discussion of the relevant issues—first and foremost so that I can learn something, and second so that my readers can! I’d be especially interested to understand:

  1. Weren’t there, like, several other claims to have produced Majoranas? What of those then?
  2. If, today, no one has convincingly demonstrated the existence of Majoranas, then do people think it more likely that they were produced but not detected, or that they weren’t even produced?
  3. How credible are the explanations as to what went wrong?
  4. Are there any broader implications for the prospects of topological QC, or Microsoft’s path to topological QC, or was this just an isolated mistake?

Another axe swung at the Sycamore

March 7th, 2021

So there’s an interesting new paper on the arXiv by Feng Pan and Pan Zhang, entitled “Simulating the Sycamore supremacy circuits.” It’s about a new tensor contraction strategy for classically simulating Google’s 53-qubit quantum supremacy experiment from Fall 2019. Using their approach, and using just 60 GPUs running for a few days, the authors say they managed to generate a million correlated 53-bit strings—meaning, strings that all agree on a specific subset of 20 or so bits—that achieve a high linear cross-entropy score.

Alas, I haven’t had time this weekend to write a “proper” blog post about this, but several people have by now emailed to ask my opinion, so I thought I’d share the brief response I sent to a journalist.

This does look like a significant advance on simulating Sycamore-like random quantum circuits! Since it’s based on tensor networks, you don’t need the literally largest supercomputer on the planet filling up tens of petabytes of hard disk space with amplitudes, as in the brute-force strategy proposed by IBM. Pan and Zhang’s strategy seems most similar to the strategy previously proposed by Alibaba, with the key difference being that the new approach generates millions of correlated samples rather than just one.

I guess my main thoughts for now are:

  1. Once you knew about this particular attack, you could evade it and get back to where we were before by switching to a more sophisticated verification test — namely, one where you not only computed a Linear XEB score for the observed samples, you also made sure that the samples didn’t share too many bits in common.  (Strangely, though, the paper never mentions this point.)
  2. The other response, of course, would just be to redo random circuit sampling with a slightly bigger quantum computer, like the ~70-qubit devices that Google, IBM, and others are now building!

Anyway, very happy for thoughts from anyone who knows more.