Archive for the ‘Complexity’ Category

Two new talks and an interview

Thursday, December 2nd, 2021
  1. A talk to UT Austin’s undergraduate math club (handwritten PDF notes) about Hao Huang’s proof of the Sensitivity Conjecture, and its implications for quantum query complexity and more. I’m still not satisfied that I’ve presented Huang’s beautiful proof as clearly and self-containedly as I possibly can, which probably just means I need to lecture on it a few more times.
  2. A Zoom talk at the QPQIS conference in Beijing (PowerPoint slides), setting out my most recent thoughts about Google’s and USTC’s quantum supremacy experiments and the continuing efforts to spoof them classically.
  3. An interview with me in Communications of the ACM, mostly about BosonSampling and the quantum lower bound for the collision problem.

Enjoy y’all!

The Acrobatics of BQP

Friday, November 19th, 2021

Just in case anyone is depressed this afternoon and needs something to cheer them up, students William Kretschmer, DeVon Ingram, and I have finally put out a new paper:

The Acrobatics of BQP

Abstract: We show that, in the black-box setting, the behavior of quantum polynomial-time (BQP) can be remarkably decoupled from that of classical complexity classes like NP. Specifically:

– There exists an oracle relative to which NPBQP⊄BQPPH, resolving a 2005 problem of Fortnow. Interpreted another way, we show that AC0 circuits cannot perform useful homomorphic encryption on instances of the Forrelation problem. As a corollary, there exists an oracle relative to which P=NP but BQP≠QCMA.

– Conversely, there exists an oracle relative to which BQPNP⊄PHBQP.

– Relative to a random oracle, PP=PostBQP is not contained in the “QMA hierarchy” QMAQMA^QMA^…, and more generally PP⊄(MIP*)(MIP*)^(MIP*)^… (!), despite the fact that MIP*=RE in the unrelativized world. This result shows that there is no black-box quantum analogue of Stockmeyer’s approximate counting algorithm.

– Relative to a random oracle, Σk+1⊄BQPΣ_k for every k.

– There exists an oracle relative to which BQP=P#P and yet PH is infinite. (By contrast, if NP⊆BPP, then PH collapses relative to all oracles.)

– There exists an oracle relative to which P=NP≠BQP=P#P.

To achieve these results, we build on the 2018 achievement by Raz and Tal of an oracle relative to which BQP⊄PH, and associated results about the Forrelation problem. We also introduce new tools that might be of independent interest. These include a “quantum-aware” version of the random restriction method, a concentration theorem for the block sensitivity of AC0 circuits, and a (provable) analogue of the Aaronson-Ambainis Conjecture for sparse oracles.

Incidentally, particularly when I’ve worked on a project with students, I’m often tremendously excited and want to shout about it from the rooftops for the students’ sake … but then I also don’t want to use this blog to privilege my own papers “unfairly.” Can anyone suggest a principle that I should follow going forward?

Scott Aaronson, when reached for comment, said…

Tuesday, November 16th, 2021

About IBM’s new 127-qubit superconducting chip: As I told New Scientist, I look forward to seeing the actual details! As far as I could see, the marketing materials that IBM released yesterday take a lot of words to say absolutely nothing about what, to experts, is the single most important piece of information: namely, what are the gate fidelities? How deep of a quantum circuit can they apply? How have they benchmarked the chip? Right now, all I have to go on is a stats page for the new chip, which reports its average CNOT error as 0.9388—in other words, close to 1, or terrible! (But see also a tweet by James Wootton, which explains that such numbers are often highly misleading when a new chip is first rolled out.) Does anyone here have more information? Update (11/17): As of this morning, the average CNOT error has been updated to 2%. Thanks to multiple commenters for letting me know!

About the new simulation of Google’s 53-qubit Sycamore chip in 5 minutes on a Sunway supercomputer (see also here): This is an exciting step forward on the classical validation of quantum supremacy experiments, and—ironically, what currently amounts to almost the same thing—on the classical spoofing of those experiments. Congratulations to the team in China that achieved this! But there are two crucial things to understand. First, “5 minutes” refers to the time needed to calculate a single amplitude (or perhaps, several correlated amplitudes) using tensor network contraction. It doesn’t refer to the time needed to generate millions of independent noisy samples, which is what Google’s Sycamore chip does in 3 minutes. For the latter task, more like a week still seems to be needed on the supercomputer. (I’m grateful to Chu Guo, a coauthor of the new work who spoke in UT Austin’s weekly quantum Zoom meeting, for clarifying this point.) Second, the Sunway supercomputer has parallel processing power equivalent to approximately ten million of your laptop. Thus, even if we agreed that Google no longer had quantum supremacy as measured by time, it would still have quantum supremacy as measured by carbon footprint! (And this despite the fact that the quantum computer itself requires a noisy, closet-sized dilution fridge.) Even so, for me the new work underscores the point that quantum supremacy is not yet a done deal. Over the next few years, I hope that Google and USTC, as well as any new entrants to this race (IBM? IonQ? Harvard? Rigetti?), will push forward with more qubits and, even more importantly, better gate fidelities leading to higher Linear Cross-Entropy scores. Meanwhile, we theorists should try to do our part by inventing new and better protocols with which to demonstrate near-term quantum supremacy—especially protocols for which the classical verification is easier.

About the new anti-woke University of Austin (UATX): In general, I’m extremely happy for people to experiment with new and different institutions, and of course I’m happy for more intellectual activity in my adopted city of Austin. And, as Shtetl-Optimized readers will know, I’m probably more sympathetic than most to the reality of the problem that UATX is trying to solve—living, as we do, in an era when one academic after another has been cancelled for ideas that a mere decade ago would’ve been considered unexceptional, moderate, center-left. Having said all that, I wish I could feel more optimistic about UATX’s prospects. I found its website heavy on free-speech rhetoric but frustratingly light on what the new university is actually going to do: what courses it will offer, who will teach them, where the campus will be, etc. etc. Arguably this is all excusable for a university still in ramp-up mode, but had I been in their shoes, I might have held off on the public launch until I had at least some sample content to offer. Certainly, the fact that Steven Pinker has quit UATX’s advisory board is a discouraging sign. If UATX asks me to get involved—to lecture there, to give them advice about their CS program, etc.—I’ll consider it as I would any other request. So far, though, they haven’t.

About the Association for Mathematical Research: Last month, some colleagues invited me to join a brand-new society called the Association for Mathematical Research. Many of the other founders (Joel Hass, Abigail Thompson, Colin Adams, Richard Borcherds, Jeff Cheeger, Pavel Etingof, Tom Hales, Jeff Lagarias, Mark Lackenby, Cliff Taubes, …) were brilliant mathematicians who I admired, they seemed like they could use a bit of theoretical computer science representation, there was no time commitment, maybe they’d eventually do something good, so I figured why not? Alas, to say that AMR has proved unpopular on Twitter would be an understatement: it’s received the same contemptuous reception that UATX has. The argument seems to be: starting a new mathematical society, even an avowedly diverse and apolitical one, is really just an implicit claim that the existing societies, like the Mathematical Association of America (MAA) and the American Mathematical Society (AMS), have been co-opted by woke true-believers. But that’s paranoid and insane! I mean, it’s not as if an AMS blog has called for the mass resignation of white male mathematicians to make room for the marginalized, or the boycott of Israeli universities, or the abolition of the criminal justice system (what to do about Kyle Rittenhouse though?). Still, even though claims of that sort of co-option are obviously far-out, rabid fantasies, yeah, I did decide to give a new organization the benefit of the doubt. AMR might well fail or languish in obscurity, just like UATX might. On the other hand, the barriers to making a positive difference for the intellectual world, the world I love, the world under constant threat from the self-certain ideologues of every side, do strike me as orders of magnitude smaller for a new professional society than they do for a new university.

Gaussian BosonSampling, higher-order correlations, and spoofing: An update

Sunday, October 10th, 2021

In my last post, I wrote (among other things) about an ongoing scientific debate between the group of Chaoyang Lu at USTC in China, which over the past year has been doing experiments that seek to demonstrate quantum supremacy via Gaussian BosonSampling; and the group of Sergio Boixo at Google, which had a recent paper on a polynomial-time classical algorithm to sample approximately from the same distributions.  I reported the facts as I understood them at the time.  Since then, though, a long call with the Google team gave me a new and different understanding, and I feel duty-bound to share that here.

A week ago, I considered it obvious that if, using a classical spoofer, you could beat the USTC experiment on a metric like total variation distance from the ideal distribution, then you would’ve completely destroyed USTC’s claim of quantum supremacy.  The reason I believed that, in turn, is a proposition that I hadn’t given a name but needs one, so let me call it Hypothesis H:

The only way a classical algorithm to spoof BosonSampling can possibly do well in total variation distance, is by correctly reproducing the high-order correlations (correlations among the occupation numbers of large numbers of modes) — because that’s where the complexity of BosonSampling lies (if it lies anywhere).

Hypothesis H had important downstream consequences.  Google’s algorithm, by the Google team’s own admission, does not reproduce the high-order correlations.  Furthermore, because of limitations on both samples and classical computation time, Google’s paper calculates the total variation distance from the ideal distribution only on the marginal distribution on roughly 14 out of 144 modes.  On that marginal distribution, Google’s algorithm does do better than the experiment in total variation distance.  Google presents a claimed extrapolation to the full 144 modes, but eyeballing the graphs, it was far from clear to me what would happen: like, maybe the spoofing algorithm would continue to win, but maybe the experiment would turn around and win; who knows?

Chaoyang, meanwhile, made a clear prediction that the experiment would turn around and win, because of

  1. the experiment’s success in reproducing the high-order correlations,
  2. the admitted failure of Google’s algorithm in reproducing the high-order correlations, and
  3. the seeming impossibility of doing well on BosonSampling without reproducing the high-order correlations (Hypothesis H).

Given everything my experience told me about the central importance of high-order correlations for BosonSampling, I was inclined to agree with Chaoyang.

Now for the kicker: it seems that Hypothesis H is false.  A classical spoofer could beat a BosonSampling experiment on total variation distance from the ideal distribution, without even bothering to reproduce the high-order correlations correctly.

This is true because of a combination of two facts about the existing noisy BosonSampling experiments.  The first fact is that the contribution from the order-k correlations falls off like 1/exp(k).  The second fact is that, due to calibration errors and the like, the experiments already show significant deviations from the ideal distribution on the order-1 and order-2 correlations.

Put these facts together and what do you find?  Well, suppose your classical spoofing algorithm takes care to get the low-order contributions to the distribution exactly right.  Just for that reason alone, it could already win over a noisy BosonSampling experiment, as judged by benchmarks like total variation distance from the ideal distribution, or for that matter linear cross-entropy.  Yes, the experiment will beat the classical simulation on the higher-order correlations.  But because those higher-order correlations are exponentially attenuated anyway, they won’t be enough to make up the difference.  The experiment’s lack of perfection on the low-order correlations will swamp everything else.

Granted, I still don’t know for sure that this is what happens — that depends on whether I believe Sergio or Chaoyang about the extrapolation of the variation distance to the full 144 modes (my own eyeballs having failed to render a verdict!).  But I now see that it’s logically possible, maybe even plausible.

So, let’s imagine for the sake of argument that Google’s simulation wins on variation distance, even though the experiment wins on the high-order correlations.  In that case, what would be our verdict: would USTC have achieved quantum supremacy via BosonSampling, or not?

It’s clear what each side could say.

Google could say: by a metric that Scott Aaronson, the coinventor of BosonSampling, thought was perfectly adequate as late as last week — namely, total variation distance from the ideal distribution — we won.  We achieved lower variation distance than USTC’s experiment, and we did it using a fast classical algorithm.  End of discussion.  No moving the goalposts after the fact.

Google could even add: BosonSampling is a sampling task; it’s right there in the name!  The only purpose of any benchmark — whether Linear XEB or high-order correlation — is to give evidence about whether you are or aren’t sampling from a distribution close to the ideal one.  But that means that, if you accept that we are doing the latter better than the experiment, then there’s nothing more to argue about.

USTC could respond: even if Scott Aaronson is the coinventor of BosonSampling, he’s extremely far from an infallible oracle.  In the case at hand, his lack of appreciation for the sources of error in realistic experiments caused him to fixate inappropriately on variation distance as the success criterion.  If you want to see the quantum advantage in our system, you have to deliberately subtract off the low-order correlations and look at the high-order correlations.

USTC could add: from the very beginning, the whole point of quantum supremacy experiments was to demonstrate a clear speedup on some benchmark — we never particularly cared which one!  That horse is out of the barn as soon as we’re talking about quantum supremacy at all — something the Google group, which itself reported the first quantum supremacy experiment in Fall 2019, again for a completely artificial benchmark — knows as well as anyone else.  (The Google team even has experience with adjusting benchmarks: when, for example, Pan and Zhang pointed out that Linear XEB as originally specified is pretty easy to spoof for random 2D circuits, the most cogent rejoinder was: OK, fine then, add an extra check that the returned samples are sufficiently different from one another, which kills Pan and Zhang’s spoofing strategy.) In that case, then, why isn’t a benchmark tailored to the high-order correlations as good as variation distance or linear cross-entropy or any other benchmark?

Both positions are reasonable and have merit — though I confess to somewhat greater sympathy for the one that appeals to my doofosity rather than my supposed infallibility!

OK, but suppose, again for the sake of argument, that we accepted the second position, and we said that USTC gets to declare quantum supremacy as long as its experiment does better than any known classical simulation at reproducing the high-order correlations.  We’d still face the question: does the USTC experiment, in fact, do better on that metric?  It would be awkward if, having won the right to change the rules in its favor, USTC still lost even under the new rules.

Sergio tells me that USTC directly reported experimental data only for up to order-7 correlations, and at least individually, the order-7 correlations are easy to reproduce on a laptop (although sampling in a way that reproduces the order-7 correlations might still be hard—a point that Chaoyang confirms, and where further research would be great). OK, but USTC also reported that their experiment seems to reproduce up to order-19 correlations. And order-19 correlations, the Google team agrees, are hard to sample consistently with on a classical computer by any currently known algorithm.

So then, why don’t we have direct data for the order-19 correlations?  The trouble is simply that it would’ve taken USTC an astronomical amount of computation time.  So instead, they relied on a statistical extrapolation from the observed strength of the lower-order correlations — there we go again with the extrapolations!  Of course, if we’re going to let Google rest its case on an extrapolation, then maybe it’s only sporting to let USTC do the same.

You might wonder: why didn’t we have to worry about any of this stuff with the other path to quantum supremacy, the one via random circuit sampling with superconducting qubits?  The reason is that, with random circuit sampling, all the correlations except the highest-order ones are completely trivial — or, to say it another way, the reduced state of any small number of output qubits is exponentially close to the maximally mixed state.  This is a real difference between BosonSampling and random circuit sampling—and even 5-6 years ago, we knew that this represented an advantage for random circuit sampling, although I now have a deeper appreciation for just how great of an advantage it is.  For it means that, with random circuit sampling, it’s easier to place a “sword in the stone”: to say, for example, here is the Linear XEB score achieved by the trivial classical algorithm that outputs random bits, and lo, our experiment achieves a higher score, and lo, we challenge anyone to invent a fast classical spoofing method that achieves a similarly high score.

With BosonSampling, by contrast, we have various metrics with which to judge performance, but so far, for none of those metrics do we have a plausible hypothesis that says “here’s the best that any polynomial-time classical algorithm can possibly hope to do, and it’s completely plausible that even a noisy current or planned BosonSampling experiment can do better than that.”

In the end, then, I come back to the exact same three goals I would’ve recommended a week ago for the future of quantum supremacy experiments, but with all of them now even more acutely important than before:

  1. Experimentally, to increase the fidelity of the devices (with BosonSampling, for example, to observe a larger contribution from the high-order correlations) — a much more urgent goal, from the standpoint of evading classical spoofing algorithms, than further increasing the dimensionality of the Hilbert space.
  2. Theoretically, to design better ways to verify the results of sampling-based quantum supremacy experiments classically — ideally, even ways that could be applied via polynomial-time tests.
  3. For Gaussian BosonSampling in particular, to get a better understanding of the plausible limits of classical spoofing algorithms, and exactly how good a noisy device needs to be before it exceeds those limits.

Thanks so much to Sergio Boixo and Ben Villalonga for the conversation, and to Chaoyang Lu and Jelmer Renema for comments on this post. Needless to say, any remaining errors are my own.

The Physics Nobel, Gaussian BosonSampling, and Dorian Abbot

Tuesday, October 5th, 2021

1. Huge congratulations to the winners of this year’s Nobel Prize in Physics: Syukuro Manabe and Klaus Hasselmann for climate modelling, and separately, Giorgio Parisi for statistical physics. While I don’t know the others, I had the great honor to get to know Parisi three years ago, when he was chair of the committee that awarded me the Tomassoni-Chisesi Prize in Physics, and when I visited Parisi’s department at Sapienza University of Rome to give the prize lecture and collect the award. I remember Parisi’s kindness, a lot of good food, and a lot of discussion of the interplay between theoretical computer science and physics. Note that, while much of Parisi’s work is beyond my competence to comment on, in computer science he’s very well-known for applying statistical physics methods to the analysis of survey propagation—an algorithm that revolutionized the study of random 3SAT when it was introduced two decades ago.


2. Two weeks ago, a group at Google put out a paper with a new efficient classical algorithm to simulate the recent Gaussian BosonSampling experiments from USTC in China. They argued that this algorithm called into question USTC’s claim of BosonSampling-based quantum supremacy. Since then, I’ve been in contact with Sergio Boixo from Google, Chaoyang Lu from USTC, and Jelmer Renema, a Dutch BosonSampling expert and friend of the blog, to try to get to the bottom of this. Very briefly, the situation seems to be that Google’s new algorithm outperforms the USTC experiment on one particular metric: namely, total variation distance from the ideal marginal distribution, if (crucially) you look at only a subset of the optical modes, say 14 modes out of 144 total. Meanwhile, though, if you look at the kth-order correlations for large values of k, then the USTC experiment continues to win. With the experiment, the correlations fall off exponentially with k but still have a meaningful, detectable signal even for (say) k=19, whereas with Google’s spoofing algorithm, you choose the k that you want to spoof (say, 2 or 3), and then the correlations become nonsense for larger k.

Now, given that you were only ever supposed to see a quantum advantage from BosonSampling if you looked at the kth-order correlations for large values of k, and given that we already knew, from the work of Leonid Gurvits, that very small marginals in BosonSampling experiments would be easy to reproduce on a classical computer, my inclination is to say that USTC’s claim of BosonSampling-based quantum supremacy still stands. On the other hand, it’s true that, with BosonSampling especially, more so than with qubit-based random circuit sampling, we currently lack an adequate theoretical understanding of what the target should be. That is, which numerical metric should an experiment aim to maximize, and how well does it have to score on that metric before it’s plausibly outperforming any fast classical algorithm? One thing I feel confident about is that, whichever metric is chosen—Linear Cross-Entropy or whatever else—it needs to capture the kth-order correlations for large values of k. No metric that’s insensitive to those correlations is good enough.


3. Like many others, I was outraged and depressed that MIT uninvited Dorian Abbot (see also here), a geophysicist at the University of Chicago, who was slated to give the Carlson Lecture in the Department of Earth, Atmospheric, and Planetary Sciences about the atmospheres of extrasolar planets. The reason for the cancellation was that, totally unrelatedly to his scheduled lecture, Abbot had argued in Newsweek and elsewhere that Diversity, Equity, and Inclusion initiatives should aim for equality for opportunity rather than equality of outcomes, a Twitter-mob decided to go after him in retaliation, and they succeeded. It should go without saying that it’s perfectly reasonable to disagree with Abbot’s stance, to counterargue—if those very concepts haven’t gone the way of floppy disks. It should also go without saying that the MIT EAPS department chair is free to bow to social-media pressure, as he did, rather than standing on principle … just like I’m free to criticize him for it. To my mind, though, cancelling a scientific talk because of the speaker’s centrist (!) political views completely, 100% validates the right’s narrative about academia, that it’s become a fanatically intolerant echo chamber. To my fellow progressive academics, I beseech thee in the bowels of Bertrand Russell: why would you commit such an unforced error?

Yes, one can imagine views (e.g., open Nazism) so hateful that they might justify the cancellation of unrelated scientific lectures by people who hold those views, as many physicists after WWII refused to speak to Werner Heisenberg. But it seems obvious to me—as it would’ve been obvious to everyone else not long ago—that no matter where a reasonable person draws the line, Abbot’s views as he expressed them in Newsweek don’t come within a hundred miles of it. To be more explicit still: if Abbot’s views justify deplatforming him as a planetary scientist, then all my quantum computing and theoretical computer science lectures deserve to be cancelled too, for the many attempts I’ve made on this blog over the past 16 years to share my honest thoughts and life experiences, to write like a vulnerable human being rather than like a university press office. While I’m sure some sneerers gleefully embrace that implication, I ask everyone else to consider how deeply they believe in the idea of academic freedom at all—keeping in mind that such a commitment only ever gets tested when there’s a chance someone might denounce you for it.

Update: Princeton’s James Madison Program has volunteered to host Abbot’s Zoom talk in place of MIT. The talk is entitled “Climate and the Potential for Life on Other Planets.” Like probably hundreds of others who heard about this only because of the attempted cancellation, I plan to attend!

Unrelated Bonus Update: Here’s a neat YouTube video put together by the ACM about me as well as David Silver of AlphaGo and AlphaZero, on the occasion of our ACM Prizes in Computing.

My ACM TechTalk on quantum supremadvantage

Wednesday, September 15th, 2021

This Erev Yom Kippur, I wish to repent for not putting enough quantum computing content on this blog. Of course, repentance is meaningless unless accompanied by genuine reform. That being the case, please enjoy the YouTube video of my ACM TechTalk from last week about quantum supremacy—although, as you’ll see if you watch the thing, I oscillate between quantum supremacy and other terms like “quantum advantage” and even “quantum supremadvantage.” This represents the first time ever that I got pushback about a talk before I’d delivered it for political reasons—the social-justice people, it turns out, are actually serious about wanting to ban the term “quantum supremacy”—but my desire to point out all the difficulties with their proposal competed with my desire not to let that issue overshadow my talk.

And there’s plenty to talk about! While regular Shtetl-Optimized readers will have already heard (or read) most of what I say, I make some new comments, including about the new paper from last week, the night before my talk (!), by the USTC group in China, where they report a quantum supremacy experiment based on random circuit sampling with a superconducting chip, this time with a record-setting 60 qubits and 24 layers of gates. On the other hand, I also stress how increasing the circuit fidelity has become a much more urgent issue than further increasing the number of qubits (or in the case of BosonSampling, the number of photons), if our goal is for these experiments to remain a couple steps ahead of classical spoofing algorithms.

Anyway, I hope you enjoy my lovingly handcrafted visuals. Over the course of this pandemic, I’ve become a full convert to writing out my talks with a stylus pen rather than PowerPointing them—not only is it faster for me, not only does it allow for continuous scrolling rather than arbitrary divisions into slides, but it enforces simplicity and concision in ways they should be enforced.

While there was only time for me to field a few questions at the end of the talk, I later supplied written answers to 52 questions (!!) that I hadn’t gotten to. If you have a question, please check to see if it’s already there, and otherwise ask away in the comments!

Thanks so much to Yan Timanovsky for inviting and organizing this talk, and to whurley for hosting it.

Open Problems Related to Quantum Query Complexity

Tuesday, September 14th, 2021

Way back in 2005, I posed Ten Semi-Grand Challenges for Quantum Computing Theory, on at least half of which I’d say there’s been dramatic progress in the 16 years since (most of the challenges were open-ended, so that it’s unclear when to count them as “solved”). I posed more open quantum complexity problems in 2010, and some classical complexity problems in 2011. In the latter cases, I’d say there’s been dramatic progress on about a third of the problems. I won’t go through the problems one by one, but feel free to ask in the comments about any that interest you.

Shall I push my luck as a problem-poser? Shall or shall not, I have.

My impetus, this time around, was a kind invitation by Travis Humble, the editor-in-chief of the new ACM Transactions on Quantum Computing, to contribute a perspective piece to that journal on the occasion of my ACM Prize. I agreed—but only on the condition that, rather than ponderously pontificate about the direction of the field, I could simply discuss a bunch of open problems that I wanted to see solved. The result is below. It’s coming soon to an arXiv near you, but Shtetl-Optimized readers get it first.

Open Problems Related to Quantum Query Complexity (11 pages, PDF)

by Scott Aaronson

Abstract: I offer a case that quantum query complexity still has loads of enticing and fundamental open problems—from relativized QMA versus QCMA and BQP versus IP, to time/space tradeoffs for collision and element distinctness, to polynomial degree versus quantum query complexity for partial functions, to the Unitary Synthesis Problem and more.

Some of the problems on my new hit-list are ones that I and others have flogged for years or even decades, but others, as far as I know, appear here for the first time. If your favorite quantum query complexity open problem, or a problem I’ve discussed in the past, is missing, that doesn’t mean that it’s been solved or is no longer interesting—it might mean I simply ran out of time or energy before I got to it.

Enjoy! And tell me what I missed or got wrong or has a trivial solution that I overlooked.

Yet more mistakes in papers

Tuesday, August 10th, 2021

Amazing Update (Aug. 19): My former PhD student Daniel Grier tells me that he, Sergey Bravyi, and David Gosset have an arXiv preprint, from February, where they give a corrected proof of my and Andris Ambainis’s claim that any k-query quantum algorithm can be simulated by an O (N1-1/2k)-query classical randomized algorithm (albeit, not of our stronger statement, about a randomized algorithm to estimate any bounded low-degree real polynomial). The reason I hadn’t known about this is that they don’t mention it in the abstract of their paper (!!). But it’s right there in Theorem 5.


In my last post, I came down pretty hard on the blankfaces: people who relish their power to persist in easily-correctable errors, to the detriment of those subject to their authority. The sad truth, though, is that I don’t obviously do better than your average blankface in my ability to resist falsehoods on early encounter with them. As one of many examples that readers of this blog might know, I didn’t think covid seemed like a big deal in early February 2020—although by mid-to-late February 2020, I’d repented of my doofosity. If I have any tool with which to unblank my face, then it’s only my extreme self-consciousness when confronted with evidence of my own stupidities—the way I’ve trained myself over decades in science to see error-correction as a or even the fundamental virtue.

Which brings me to today’s post. Continuing what’s become a Shtetl-Optimized tradition—see here from 2014, here from 2016, here from 2017—I’m going to fess up to two serious mistakes in research papers on which I was a coauthor.


In 2015, Andris Ambainis and I had a STOC paper entitled Forrelation: A Problem that Optimally Separates Quantum from Classical Computing. We gave two main results there:

  1. A Ω((√N)/log(N)) lower bound on the randomized query complexity of my “Forrelation” problem, which was known to be solvable with only a single quantum query.
  2. A proposed way to take any k-query quantum algorithm that queries an N-bit string, and simulate it using only O(N1-1/2k) classical randomized queries.

Later, Bansal and Sinha and independently Sherstov, Storozhenko, and Wu showed that a k-query generalization of Forrelation, which I’d also defined, requires ~Ω(N1-1/2k) classical randomized queries, in line with my and Andris’s conjecture that k-fold Forrelation optimally separates quantum and classical query complexities.

A couple months ago, alas, my former grad school officemate Andrej Bogdanov, along with Tsun Ming Cheung and Krishnamoorthy Dinesh, emailed me and Andris to say that they’d discovered an error in result 2 of our paper (result 1, along with the Bansal-Sinha and Sherstov-Storozhenko-Wu extensions of it, remained fine). So, adding our own names, we’ve now posted a preprint on ECCC that explains the error, while also showing how to recover our result for the special case k=1: that is, any 1-query quantum algorithm really can be simulated using only O(√N) classical randomized queries.

Read the preprint if you really want to know the details of the error, but to summarize it in my words: Andris and I used a trick that we called “variable-splitting” to handle variables that have way more influence than average on the algorithm’s acceptance probability. Alas, variable-splitting fails to take care of a situation where there are a bunch of variables that are non-influential individually, but that on some unusual input string, can “conspire” in such a way that their signs all line up and their contribution overwhelms those from the other variables. A single mistaken inequality fooled us into thinking such cases were handled, but an explicit counterexample makes the issue obvious.

I still conjecture that my original guess was right: that is, I conjecture that any problem solvable with k quantum queries is solvable with O(N1-1/2k) classical randomized queries, so that k-fold Forrelation is the extremal example, and so that no problem has constant quantum query complexity but linear randomized query complexity. More strongly, I reiterate the conjecture that any bounded degree-d real polynomial, p:{0,1}N→[0,1], can be approximated by querying only O(N1-1/d) input bits drawn from some suitable distribution. But proving these conjectures, if they’re true, will require a new algorithmic idea.


Now for the second mea culpa. Earlier this year, my student Sabee Grewal and I posted a short preprint on the arXiv entitled Efficient Learning of Non-Interacting Fermion Distributions. In it, we claimed to give a classical algorithm for reconstructing any “free fermionic state” |ψ⟩—that is, a state of n identical fermionic particles, like electrons, each occupying one of m>n possible modes, that can be produced using only “fermionic beamsplitters” and no interaction terms—and for doing so in polynomial time and using a polynomial number of samples (i.e., measurements of where all the fermions are, given a copy of |ψ⟩). Alas, after trying to reply to confused comments from readers and reviewers (albeit, none of them exactly putting their finger on the problem), Sabee and I were able to figure out that we’d done no such thing.

Let me explain the error, since it’s actually really interesting. In our underlying problem, we’re trying to find a collection of unit vectors, call them |v1⟩,…,|vm⟩, in Cn. Here, again, n is the number of fermions and m>n is the number of modes. By measuring the “2-mode correlations” (i.e., the probability of finding a fermion in both mode i and mode j), we can figure out the approximate value of |⟨vi|vj⟩|—i.e., the absolute value of the inner product—for any i≠j. From that information, we want to recover |v1⟩,…,|vm⟩ themselves—or rather, their relative configuration in n-dimensional space, isometries being irrelevant.

It seemed to me and Sabee that, if we knew ⟨vi|vj⟩ for all i≠j, then we’d get linear equations that iteratively constrained each |vj⟩ in terms of ⟨vi|vj⟩ for j<i, so all we’d need to do is solve those linear systems, and then (crucially, and this was the main work we did) show that the solution would be robust with respect to small errors in our estimates of each ⟨vi|vj⟩. It seemed further to us that, while it was true that the measurements only revealed |⟨vi|vj⟩| rather than ⟨vi|vj⟩ itself, the “phase information” in ⟨vi|vj⟩ was manifestly irrelevant, as it in any case depended on the irrelevant global phases of |vi⟩ and |vj⟩ themselves.

Alas, it turns out that the phase information does matter. As an example, suppose I told you only the following about three unit vectors |u⟩,|v⟩,|w⟩ in R3:

|⟨u|v⟩| = |⟨u|w⟩| = |⟨v|w⟩| = 1/2.

Have I thereby determined these vectors up to isometry? Nope! In one class of solution, all three vectors belong to the same plane, like so:

|u⟩=(1,0,0),
|v⟩=(1/2,(√3)/2,0),
|w⟩=(-1/2,(√3)/2,0).

In a completely different class of solution, the three vectors don’t belong to the same plane, and instead look like three edges of a tetrahedron meeting at a vertex:

|u⟩=(1,0,0),
|v⟩=(1/2,(√3)/2,0),
|w⟩=(1/2,1/(2√3),√(2/3)).

These solutions correspond to different sign choices for |⟨u|v⟩|, |⟨u|w⟩|, and |⟨v|w⟩|—choices that collectively matter, even though each of them is individually irrelevant.

It follows that, even in the special case where the vectors are all real, the 2-mode correlations are not enough information to determine the vectors’ relative positions. (Well, it takes some more work to convert this to a counterexample that could actually arise in the fermion problem, but that work can be done.) And alas, the situation gets even gnarlier when, as for us, the vectors can be complex.

Any possible algorithm for our problem will have to solve a system of nonlinear equations (albeit, a massively overconstrained system that’s guaranteed to have a solution), and it will have to use 3-mode correlations (i.e., statistics of triples of fermions), and quite possibly 4-mode correlations and above.

But now comes the good news! Googling revealed that, for reasons having nothing to do with fermions or quantum physics, problems extremely close to ours had already been studied in classical machine learning. The key term here is “Determinantal Point Processes” (DPPs). A DPP is a model where you specify an m×m matrix A (typically symmetric or Hermitian), and then the probabilities of various events are given by the determinants of various principal minors of A. Which is precisely what happens with fermions! In terms of the vectors |v1⟩,…,|vm⟩ that I was talking about before, to make this connection we simply let A be the m×m covariance matrix, whose (i,j) entry equals ⟨vi|vj⟩.

I first learned of this remarkable correspondence between fermions and DPPs a decade ago, from a talk on DPPs that Ben Taskar gave at MIT. Immediately after the talk, I made a mental note that Taskar was a rising star in theoretical machine learning, and that his work would probably be relevant to me in the future. While researching this summer, I was devastated to learn that Taskar died of heart failure in 2013, in his mid-30s and only a couple of years after I’d heard him speak.

The most relevant paper for me and Sabee was called An Efficient Algorithm for the Symmetric Principal Minor Assignment Problem, by Rising, Kulesza, and Taskar. Using a combinatorial algorithm based on minimum spanning trees and chordless cycles, this paper nearly solves our problem, except for two minor details:

  1. It doesn’t do an error analysis, and
  2. It considers complex symmetric matrices, whereas our matrix A is Hermitian (i.e., it equals its conjugate transpose, not its transpose).

So I decided to email Alex Kulezsa, one of Taskar’s surviving collaborators who’s now a research scientist at Google NYC, to ask his thoughts about the Hermitian case. Alex kindly replied that they’d been meaning to study that case—a reviewer had even asked about it!—but they’d ran into difficulties and didn’t know what it was good for. I asked Alex whether he’d like to join forces with me and Sabee in tackling the Hermitian case, which (I told him) was enormously relevant in quantum physics. To my surprise and delight, Alex agreed.

So we’ve been working on the problem together, making progress, and I’m optimistic that we’ll have some nice result. By using the 3-mode correlations, at least “generically” we can recover the entries of the matrix A up to complex conjugation, but further ideas will be needed to resolve the complex conjugation ambiguity, to whatever extent it actually matters.

In short: on the negative side, there’s much more to the problem of learning a fermionic state than we’d realized. But on the positive side, there’s much more to the problem than we’d realized! As with the simulation of k-query quantum algorithms, my coauthors and I would welcome any ideas. And I apologize to anyone who was misled by our premature (and hereby retracted) claims.


Update (Aug. 11): Here’s a third bonus retraction, which I thank my colleague Mark Wilde for bringing to my attention. Way back in 2005, in my NP-complete Problems and Physical Reality survey article, I “left it as an exercise for the reader” to prove that BQPCTC, or quantum polynomial time augmented with Deutschian closed timelike curves, is contained in a complexity class called SQG (Short Quantum Games). While it turns out to be true that BQPCTC ⊆ SQG—as follows from my and Watrous’s 2008 result that BQPCTC = PSPACE, combined with Gutoski and Wu’s 2010 result that SQG = PSPACE—it’s not something for which I could possibly have had a correct proof back in 2005. I.e., it was a harder exercise than I’d intended!

Slowly emerging from blog-hibervacation

Wednesday, July 21st, 2021

Alright everyone:

  1. Victor Galitski has an impassioned rant against out-of-control quantum computing hype, which I enjoyed and enthusiastically recommend, although I wished Galitski had engaged a bit more with the strongest arguments for optimism (e.g., the recent sampling-based supremacy experiments, the extrapolations that show gate fidelities crossing the fault-tolerance threshold within the next decade). Even if I’ve been saying similar things on this blog for 15 years, I clearly haven’t been doing so in a style that works for everyone. Quantum information needs as many people as possible who will tell the truth as best they see it, unencumbered by any competing interests, and has nothing legitimate to fear from that. The modern intersection of quantum theory and computer science has raised profound scientific questions that will be with us for decades to come. It’s a lily that need not be gilded with hype.
  2. Last month Limaye, Srinivasan, and Tavenas posted an exciting preprint to ECCC, which apparently proves the first (slightly) superpolynomial lower bound on the size of constant-depth arithmetic circuits, over fields of characteristic 0. Assuming it’s correct, this is another small victory in the generations-long war against the P vs. NP problem.
  3. I’m grateful to the Texas Democratic legislators who fled the state to prevent the legislature, a couple miles from my house, having a quorum to enact new voting restrictions, and who thereby drew national attention to the enormity of what’s at stake. It should go without saying that, if a minority gets to rule indefinitely by forcing through laws to suppress the votes of a majority that would otherwise unseat it, thereby giving itself the power to force through more such laws, etc., then we no longer live in a democracy but in a banana republic. And there’s no symmetry to the situation: no matter how terrified you (or I) might feel about wokeists and their denunciation campaigns, the Democrats have no comparable effort to suppress Republican votes. Alas, I don’t know of any solutions beyond the obvious one, of trying to deal the conspiracy-addled grievance party crushing defeats in 2022 and 2024.
  4. Added: Here’s the video of my recent Astral Codex Ten ask-me-anything session.

Open thread on new quantum supremacy claims

Sunday, July 4th, 2021

Happy 4th to those in the US!

The group of Chaoyang Lu and Jianwei Pan, based at USTC in China, has been on a serious quantum supremacy tear lately. Recall that last December, USTC announced the achievement of quantum supremacy via Gaussian BosonSampling, with 50-70 detected photons—the second claim of sampling-based quantum supremacy, after Google’s in Fall 2019. However, skeptics then poked holes in the USTC claim, showing how they could spoof the results with a classical computer, basically by reproducing the k-photon correlations for relatively small values of k. Debate over the details continues, but the Chinese group seeks to render the debate largely moot with a new and better Gaussian BosonSampling experiment, with 144 modes and up to 113 detected photons. They say they were able to measure k-photon correlations for k up to about 19, which if true would constitute a serious obstacle to the classical simulation strategies that people discussed for the previous experiment.

In the meantime, though, an overlapping group of authors had put out another paper the day before (!) reporting a sampling-based quantum supremacy experiment using superconducting qubits—extremely similar to what Google did (the same circuit depth and everything), except now with 56 qubits rather than 53.

I confess that I haven’t yet studied either paper in detail—among other reasons, because I’m on vacation with my family at the beach, and because I’m trying to spend what work-time I have on my own projects. But anyone who has read them, please use the comments of this post to discuss! Hopefully I’ll learn something.

To confine myself to some general comments: since Google’s announcement in Fall 2019, I’ve consistently said that sampling-based quantum supremacy is not yet a done deal. I’ve said that quantum supremacy seems important enough to want independent replications, and demonstrations in other hardware platforms like ion traps and photonics, and better gate fidelity, and better classical hardness, and better verification protocols. Most of all, I’ve said that we needed a genuine dialogue between the “quantum supremacists” and the classical skeptics: the former doing experiments and releasing all their data, the latter trying to design efficient classical simulations for those experiments, and so on in an iterative process. Just like in applied cryptography, we’d only have real confidence in a quantum supremacy claim once it had survived at least a few years of attacks by skeptics. So I’m delighted that this is precisely what’s now happening. USTC’s papers are two new volleys in this back-and-forth; we all eagerly await the next volley, whichever side it comes from.

While I’ve been trying for years to move away from the expectation that I blog about each and every QC announcement that someone messages me about, maybe I’ll also say a word about the recent announcement by IBM of a quantum advantage in space complexity (see here for popular article and here for arXiv preprint). There appears to be a nice theoretical result here, about the ability to evaluate any symmetric Boolean function with a single qubit in a branching-program-like model. I’d love to understand that result better. But to answer the question I received, this is another case where, once you know the protocol, you know both that the experiment can be done and exactly what its result will be (namely, the thing predicted by QM). So I think the interest is almost entirely in the protocol itself.