Archive for the ‘Quantum’ Category

Review of Vivek Wadhwa’s Washington Post column on quantum computing

Tuesday, February 13th, 2018

Various people pointed me to a Washington Post piece by Vivek Wadhwa, entitled “Quantum computers may be more of an immiment threat than AI.”  I know I’m late to the party, but in the spirit of Pete Wells’ famous New York Times “review” of Guy Fieri’s now-closed Times Square restaurant, I have a few questions that have been gnawing at me:

Mr. Wadhwa, when you decided to use the Traveling Salesman Problem as your go-to example of a problem that quantum computers can solve quickly, did the thought ever cross your mind that maybe you should look this stuff up first—let’s say, on Wikipedia?  Or that you should email one person—just one, anywhere on the planet—who works in quantum algorithms?

When you wrote of the Traveling Salesman Problem that “[i]t would take a laptop computer 1,000 years to compute the most efficient route between 22 cities”—how confident are you about that?  Willing to bet your house?  Your car?  How much would it blow your mind if I told you that a standard laptop, running a halfway decent algorithm, could handle 22 cities in a fraction of a second?

When you explained that quantum computing is “equivalent to opening a combination lock by trying every possible number and sequence simultaneously,” where did this knowledge come from?  Did it come from the same source you consulted before you pronounced the death of Bitcoin … in January 2016?

Had you wanted to consult someone who knew the first thing about quantum computing, the subject of your column, would you have been able to use a search engine to find one?  Or would you have simply found another “expert,” in the consulting or think-tank worlds, who “knew” the same things about quantum computing that you do?

Incidentally, when you wrote that quantum computing “could pose a greater burden on businesses than the Y2K computer bug did toward the end of the ’90s,” were you trying to communicate how large the burden might be?

And when you wrote that

[T]here is substantial progress in the development of algorithms that are “quantum safe.” One promising field is matrix multiplication, which takes advantage of the techniques that allow quantum computers to be able to analyze so much information.

—were you generating random text using one of those Markov chain programs?  If not, then what were you referring to?

Would you agree that the Washington Post has been a leader in investigative journalism exposing Trump’s malfeasance?  Do you, like me, consider them one of the most important venues on earth for people to be able to trust right now?  How does it happen that the Washington Post publishes a quantum computing piece filled with errors that would embarrass a high-school student doing a term project (and we won’t even count the reference to Stephen “Hawkings”—that’s a freebie)?

Were the fact-checkers home with the flu?  Did they give your column a pass simply because it was “perspective” rather than news?  Or did they trust you as a widely-published technology expert?  How does one become such an expert, anyway?

Thanks!

Three updates

Monday, February 5th, 2018
  1. I was extremely sorry to learn about the loss of Joe Polchinski, a few days ago, to brain cancer.  Joe was a leading string theorist, one of the four co-discoverers of the AMPS firewall paradox, and one of the major figures in the Simons It from Qubit collaboration that I’ve been happy to be part of since its inception.  I regret that I didn’t get to know Joe as well as I should have, but he was kind to me in all of our interactions.  He’ll be missed by all who knew him.
  2. Edge has posted what will apparently be its final Annual Edge Question: “What is the last question?”  They asked people to submit just a single, one sentence question “for which they’ll be remembered,” with no further explanation or elaboration.  You can read mine, which not surprisingly is alphabetically the first.  I tried to devise a single question that gestured toward the P vs. NP problem, and the ultimate physical limits of computation, and the prospects for superintelligent AI, and the enormity of what could be Platonically lying in wait for us within finite but exponentially search spaces, and the eternal nerd’s conundrum, of the ability to get the right answers to clearly-stated questions being so ineffectual in the actual world.  I’m not thrilled with the result, but reading through the other questions makes it clear just how challenging it is to ask something that doesn’t boil down to: “When will the rest of the world recognize the importance of my research topic?”
  3. I’m now reaping the fruits of my decision to take a year-long sabbatical from talking to journalists.  Ariel Bleicher, a writer for Quanta magazine, asked to interview me for an article she was writing about the difficulty of establishing quantum supremacy.  I demurred, mentioning my sabbatical, and pointed her to others she could ask instead.  Well, last week the article came out, and while much of it is quite good, it opens with an extended presentation of a forehead-bangingly wrong claim by Cristian Calude: namely, that the Deutsch-Jozsa problem (i.e. computing the parity of two bits) can be solved with one query even by a classical algorithm, so that (in effect) one of the central examples used in introductory quantum computing courses is a lie.  This claim is based on a 2006 paper wherein, with all the benefits of theft over honest toil, Calude changes the query model so that you can evaluate not just the original oracle function f, but an extension of f to the complex numbers (!).  Apparently Calude justifies this by saying that Deutsch also changed the problem, by allowing it to be solved with a quantum computer, so he gets to change the problem as well.  The difference, of course, is that the quantum query complexity model is justified by its relevance for quantum algorithms, and (ultimately) by quantum mechanics being true of our world.  Calude’s model, by contrast, is (as far as I can tell) pulled out of thin air and justified by nothing.  Anyway, I regard this incident as entirely, 100% my fault, and 0% Ariel’s.  How was she to know that, while there are hundreds of knowledgeable quantum computing experts to interview, almost all of them are nice and polite?  Anyway, this has led me to a revised policy: while I’ll still decline interviews, news organizations should feel free to run completed quantum computing pieces by me for quick fact checks.

Interpretive cards (MWI, Bohm, Copenhagen: collect ’em all)

Saturday, February 3rd, 2018

I’ve been way too distracted by actual research lately from my primary career as a nerd blogger—that’s what happens when you’re on sabbatical.  But now I’m sick, and in no condition to be thinking about research.  And this morning, in a thread that had turned to my views on the interpretation of quantum mechanics called “QBism,” regular commenter Atreat asked me the following pointed question:

Scott, what is your preferred interpretation of QM? I don’t think I’ve ever seen you put your cards on the table and lay out clearly what interpretation(s) you think are closest to the truth. I don’t think your ghost paper qualifies as an answer, BTW. I’ve heard you say you have deep skepticism about objective collapse theories and yet these would seemingly be right up your philosophical alley so to speak. If you had to bet on which interpretation was closest to the truth, which one would you go with?

Many people have asked me some variant of the same thing.  As it happens, I’d been toying since the summer with a huge post about my views on each major interpretation, but I never quite got it into a form I wanted.  By contrast, it took me only an hour to write out a reply to Atreat, and in the age of social media and attention spans measured in attoseconds, many readers will probably prefer that short reply to the huge post anyway.  So then I figured, why not promote it to a full post and be done with it?  So without further ado:


Dear Atreat,

It’s no coincidence that you haven’t seen me put my cards on the table with a favored interpretation of QM!

There are interpretations (like the “transactional interpretation”) that make no sense whatsoever to me.

There are “interpretations” like dynamical collapse that aren’t interpretations at all, but proposals for new physical theories.  By all means, let’s test QM on larger and larger systems, among other reasons because it could tell us that some such theory is true or—vastly more likely, I think—place new limits on it! (People are trying.)

Then there’s the deBroglie-Bohm theory, which does lay its cards on the table in a very interesting way, by proposing a specific evolution rule for hidden variables (chosen to match the predictions of QM), but which thereby opens itself up to the charge of non-uniqueness: why that rule, as opposed to a thousand other rules that someone could write down?  And if they all lead to the same predictions, then how could anyone ever know which rule was right?

And then there are dozens of interpretations that seem to differ from one of the “main” interpretations (Many-Worlds, Copenhagen, Bohm) mostly just in the verbal patter.

As for Copenhagen, I’ve described it as “shut-up and calculate except without ever shutting up about it”!  I regard Bohr’s writings on the subject as barely comprehensible, and Copenhagen as less of an interpretation than a self-conscious anti-interpretation: a studied refusal to offer any account of the actual constituents of the world, and—most of all—an insistence that if you insist on such an account, then that just proves that you cling naïvely to a classical worldview, and haven’t grasped the enormity of the quantum revolution.

But the basic split between Many-Worlds and Copenhagen (or better: between Many-Worlds and “shut-up-and-calculate” / “QM needs no interpretation” / etc.), I regard as coming from two fundamentally different conceptions of what a scientific theory is supposed to do for you.  Is it supposed to posit an objective state for the universe, or be only a tool that you use to organize your experiences?

Also, are the ultimate equations that govern the universe “real,” while tables and chairs are “unreal” (in the sense of being no more than fuzzy approximate descriptions of certain solutions to the equations)?  Or are the tables and chairs “real,” while the equations are “unreal” (in the sense of being tools invented by humans to predict the behavior of tables and chairs and whatever else, while extraterrestrials might use other tools)?  Which level of reality do you care about / want to load with positive affect, and which level do you want to denigrate?

This is not like picking a race horse, in the sense that there might be no future discovery or event that will tell us who was closer to the truth.  I regard it as conceivable that superintelligent AIs will still argue about the interpretation of QM … or maybe that God and the angels argue about it now.

Indeed, about the only thing I can think of that might definitively settle the debate, would be the discovery of an even deeper level of description than QM—but such a discovery would “settle” the debate only by completely changing the terms of it.

I will say this, however, in favor of Many-Worlds: it’s clearly and unequivocally the best interpretation of QM, as long as we leave ourselves out of the picture!  I.e., as long as we say that the goal of physics is to give the simplest, cleanest possible mathematical description of the world that somewhere contains something that seems to correspond to observation, and we’re willing to shunt as much metaphysical weirdness as needed to those who worry themselves about details like “wait, so are we postulating the physical existence of a continuum of slightly different variants of me, or just an astronomically large finite number?” (Incidentally, Max Tegmark’s “mathematical multiverse” does even better than MWI by this standard.  Tegmark is the one waiting for you all the way at the bottom of the slippery slope of always preferring Occam’s Razor over trying to account for the specificity of the observed world.)  It’s no coincidence, I don’t think, that MWI is so popular among those who are also eliminativists about consciousness.

When I taught my undergrad Intro to Quantum Information course last spring—for which lecture notes are coming soon, by the way!—it was striking how often I needed to resort to an MWI-like way of speaking when students got confused about measurement and decoherence. (“So then we apply this unitary transformation U that entangles the system and environment, and we compute a partial trace over the environment qubits, and we see that it’s as if the system has been measured, though of course we could in principle reverse this by applying U-1 … oh shoot, have I just conceded MWI?”)

On the other hand, when (at the TAs’ insistence) we put an optional ungraded question on the final exam that asked students their favorite interpretation of QM, we found that there was no correlation whatsoever between interpretation and final exam score—except that students who said they didn’t believe any interpretation at all, or that the question was meaningless or didn’t matter, scored noticeably higher than everyone else.

Anyway, as I said, MWI is the best interpretation if we leave ourselves out of the picture.  But you object: “OK, and what if we don’t leave ourselves out of the picture?  If we dig deep enough on the interpretation of QM, aren’t we ultimately also asking about the ‘hard problem of consciousness,’ much as some people try to deny that? So for example, what would it be like to be maintained in a coherent superposition of thinking two different thoughts A and B, and then to get measured in the |A⟩+|B⟩, |A⟩-|B⟩ basis?  Would it even be like anything?  Or is there something about our consciousness that depends on decoherence, irreversibility, full participation in the arrow of the time, not living in an enclosed little unitary box like AdS/CFT—something that we’d necessarily destroy if we tried to set up a large-scale interference experiment on our own brains, or any other conscious entities?  If so, then wouldn’t that point to a strange sort of reconciliation of Many-Worlds with Copenhagen—where as soon as we had a superposition involving different subjective experiences, for that very reason its being a superposition would be forevermore devoid of empirical consequences, and we could treat it as just a classical probability distribution?”

I’m not sure, but The Ghost in the Quantum Turing Machine will probably have to stand as my last word (or rather, last many words) on those questions for the time being.

Practicing the modus ponens of Twitter

Monday, January 29th, 2018

I saw today that Ryan Lackey generously praised my and Zach Weinersmith’s quantum computing SMBC comic on Twitter:

Somehow this SMBC comic is the best explanation of quantum computing for non-professionals that I’ve ever found

To which the venture capitalist Matthew Ocko replied, in another tweet:

Except Scott Aaronson is a surly little troll who has literally never built anything at all of meaning. He’s a professional critic of braver people.  So, no, this is not a good explanation – anymore than Jeremy Rifkin on CRISPR would be… 🙄

Now, I don’t mind if Ocko hates me, and also hates my and Zach’s comic.  What’s been bothering me is just the logic of his tweet.  Like: what did he have in his head when he wrote the word “So”?  Let’s suppose for the sake of argument that I’m a “surly little troll,” and an ax murderer besides.  How does it follow that my explanation of quantum computing wasn’t good?  To reach that stop in proposition-space, wouldn’t one still need to point to something wrong with the explanation?

But I’m certain that my inability to understand this is just another of my many failings.  In a world where Trump is president, bitcoin is valued at $11,000 when I last checked, and the attack-tweet has fully replaced the argument, it’s obvious that those of us who see a word like “so” or “because,” and start looking for the inferential step, are merely insufficiently brave.  For godsakes, I’m not even on Twitter!  I’m a sclerotic dinosaur who needs to get with the times.

But maybe I, too, could learn the art of the naked ad-hominem.  Let me try: from a Google search, we learn that Ocko is an enthusiastic investor in D-Wave.  Is it possible he’s simply upset that there’s so much excitement right now in experimental quantum computing—including “things of meaning” being built by brave people, at Google and IBM and Rigetti and IonQ and elsewhere—but that virtually none of this involves D-Wave, whose devices remain interesting from various physics and engineering standpoints, but still fail to achieve any clear quantum speedups, just as the professional critics predicted?  Is he upset that the brave system-builders who are racing finally to achieve quantum computational supremacy over the next year, are the ones who actually interacted with academic researchers (sorry: surly little trolls), and listened to what they said?  Who understood, for example, why scaling up to 50+ qubits only made a lot of sense once you had one or two qubits that at least behaved well enough in isolation—which, after years of heroic effort, many of these system-builders now do?

How’d I do?  Was there still too much argument there for the world of 2018?

John Preskill, laziness enabler

Thursday, January 4th, 2018

The purpose of this post is just to call everyone’s attention to a beautiful and accessible new article by John Preskill: Quantum Computing in the NISQ era and beyond.  The article is based on John’s keynote address at the recent “Q2B” (Quantum Computing for Business) conference, which I was unfortunately unable to attend.  Here’s the abstract:

Noisy Intermediate-Scale Quantum (NISQ) technology will be available in the near future. Quantum computers with 50-100 qubits may be able to perform tasks which surpass the capabilities of today’s classical digital computers, but noise in quantum gates will limit the size of quantum circuits that can be executed reliably. NISQ devices will be useful tools for exploring many-body quantum physics, and may have other useful applications, but the 100-qubit quantum computer will not change the world right away — we should regard it as a significant step toward the more powerful quantum technologies of the future. Quantum technologists should continue to strive for more accurate quantum gates and, eventually, fully fault-tolerant quantum computing.

Did you ever wish you had something even better than a clone: namely, someone who writes exactly what you would’ve wanted to write, on a topic people keep asking you to write about, but ten times better than you would’ve written it?  To all journalists and others who ask me over the coming year about the application potential for near-term quantum computers, I can now simply respond with a link.

Superposition your mouse over these five exciting QC links!

Friday, November 3rd, 2017

(1) My TEDx talk from Dresden, entitled “What Quantum Computing Isn’t,” is finally up on YouTube.  For regular Shtetl-Optimized readers, there’s unlikely to be much that’s new here: it’s basically 15 minutes of my usual spiel, packaged for mass consumption.  But while it went over well with the live audience, right now the only comment on the video is—I quote—“uuuuuuuuuuuuuuu,” from user “imbatman8472.”  So if you feel so inclined, go over there, watch it, and try to start a more contentful discussion!  Thanks so much to Andrés Goens, and everyone else in Dresden, for inviting me there and hosting a great visit.

(2) On December 4-6, there’s going to be a new conference in Mountain View, called Q2B (Quantum Computing for Business).  There, if it interests you, you can hear about the embryonic QC industry, from some of the major players at Google, IBM, Microsoft, academia, and government, as well as some of the QC startups (like IonQ) that have blossomed over the last few years.  Oh yes, and D-Wave.  The keynote speaker will be John Preskill; Google’s John Martinis and IBM’s Jerry Chow will also be giving talks.  I regret that another commitment will prevent me from attending myself, but I hope to attend next year’s iteration.  (Full disclosure: I’m a scientific adviser to QC Ware, the firm that’s organizing the conference.)

(3) On October 24, the House Science Committee heard three hours of testimony—you can watch it all here—about the need for quantum information research and the danger of the US falling behind China.  In what I believe is my first entry in the Congressional record, I’m quoted (for something totally incidental) at 1:09.  John Preskill was mostly just delighted that the witness, Jim Kurose, referred to me as a “physicist.”

(4) For several years, people have been asking me whether Bitcoin is resistant against quantum attack.  Now there’s finally an expert analysis, by Aggarwal et al., that looks into exactly that question.  Two-sentence summary: the proof-of-work is probably fine, although Grover’s algorithm can of course be used against it, which might eventually necessitate adjusting the difficulty parameter to account for that, and/or migrating from a pure preimage search task to collision-finding, where my result with Yaoyun Shi showed that quantum computers offer “only” an n2/3 black-box speedup over classical computers, rather than a square-root speedup.  The scheme for signing the transactions, which is currently based on elliptic curve cryptography, is the real danger point, but again one could address that by migrating to a post-quantum signature scheme.  My main comment about the matter is that, if I’d invested in Bitcoin when I first learned about it, I’d be rich now.

(5) In the first significant victory for my plan to spend a whole sabbatical year just writing up unwritten papers, I’ve got a new paper out today: Shadow Tomography of Quantum States.  Comments extremely welcome!

Grad students and postdocs and faculty sought

Saturday, October 28th, 2017

I’m eagerly seeking PhD students and postdocs to join our Quantum Information Center at UT Austin, starting in Fall 2018.  We’re open to any theoretical aspects of quantum information, although if you wanted to work with me personally, then areas close to computer science would be the closest fit.  I’m also able to supervise PhD students in physics, but am not directly involved with admissions to the physics department: this is a discussion we would have after you were already admitted to UT.

I, along with my theoretical computer science colleagues at UT Austin, am also open to outstanding students and postdocs in classical complexity theory. My wife, Dana Moshkovitz, tells me that she and David Zuckerman in particular are looking for a postdoc in the areas of pseudorandomness and derandomization (and for PhD students as well).

If you want to apply to the UTCS PhD program, please visit here.  The deadline is December 15.  If you specify that you want to work on quantum computing and information, and/or with me, then I’ll be sure to see your application.  Emailing faculty at this stage doesn’t help; we won’t “estimate your chances” or even look at your qualifications until we can see all the applications together.

If you want to apply for a postdoc with me, here’s what to do:

  • Email me introducing yourself (if I don’t already know you), and include your CV, your thesis (if you already have one), and up to 3 representative papers.  Do this even if you already emailed me before.
  • Arrange for two recommendation letters to be emailed to me.

Let’s set a deadline for postdoc applications of, I dunno, December 15?

In addition to the above, I’m happy to announce that the UT CS department is looking to hire a new faculty member in quantum computing and information—most likely a junior person.  The UT physics department is also looking to hire quantum information faculty members, with a focus on a senior-level experimentalist right now.  If you’re interested in these opportunities, just email me; I can put you in touch with the relevant people.

All in all, this is shaping up to be the most exciting era for quantum computing and information in Austin since a group of UT students, postdocs, and faculty including David Deutsch, John Wheeler, Wojciech Zurek, Bill Wootters, and Ben Schumacher laid much of the intellectual foundation of the field in the late 1970s and early 1980s.  We hope you’ll join us.  Hook ’em Hadamards!


Unrelated Announcements: Avi Wigderson has released a remarkable 368-page book, Mathematics and Computation, for free on the web.  This document surveys pretty much the entire current scope of theoretical computer science, in a way only Avi, our field’s consummate generalist, could do.  It also sets out Avi’s vision for the future and his sociological thoughts about TCS and its interactions with neighboring fields.  I was a reviewer on the manuscript, and I recommend it to anyone looking for a panoramic view of TCS.

In other news, my UT friend and colleague Adam Klivans, and his student Surbhi Goel, have put out a preprint entitled Learning Depth-Three Neural Networks in Polynomial Time.  (Beware: what the machine learning community calls “depth three,” is what the TCS community would call “depth two.”)  This paper learns real-valued neural networks in the so-called p-concept model of Kearns and Schapire, and thereby evades a 2006 impossibility theorem of Klivans and Sherstov, which showed that efficiently learning depth-2 threshold circuits would require breaking cryptographic assumptions.  More broadly, there’s been a surge of work in the past couple years on explaining the success of deep learning methods (methods whose most recent high-profile victory was, of course, AlphaGo Zero).  I’m really hoping to learn more about this direction during my sabbatical this year—though I’ll try and take care not to become another deep learning zombie, chanting “artificial BRAINSSSS…” with outstretched arms.

2^n is exponential, but 2^50 is finite

Sunday, October 22nd, 2017

Unrelated Update (Oct. 23) I still feel bad that there was no time for public questions at my “Theoretically Speaking” talk in Berkeley, and also that the lecture hall was too small to accomodate a large fraction of the people who showed up. So, if you’re someone who came there wanting to ask me something, go ahead and ask in the comments of this post.


During my whirlwind tour of the Bay Area, questions started pouring in about a preprint from a group mostly at IBM Yorktown Heights, entitled Breaking the 49-Qubit Barrier in the Simulation of Quantum Circuits.  In particular, does this paper make a mockery of everything the upcoming quantum supremacy experiments will try to achieve, and all the theorems about them that we’ve proved?

Following my usual practice, let me paste the abstract here, so that we have the authors’ words in front of us, rather than what a friend of a friend said a popular article reported might have been in the paper.

With the current rate of progress in quantum computing technologies, 50-qubit systems will soon become a reality.  To assess, refine and advance the design and control of these devices, one needs a means to test and evaluate their fidelity. This in turn requires the capability of computing ideal quantum state amplitudes for devices of such sizes and larger.  In this study, we present a new approach for this task that significantly extends the boundaries of what can be classically computed.  We demonstrate our method by presenting results obtained from a calculation of the complete set of output amplitudes of a universal random circuit with depth 27 in a 2D lattice of 7 × 7 qubits.  We further present results obtained by calculating an arbitrarily selected slice of 237 amplitudes of a universal random circuit with depth 23 in a 2D lattice of 8×7 qubits.  Such calculations were previously thought to be impossible due to impracticable memory requirements. Using the methods presented in this paper, the above simulations required 4.5 and 3.0 TB of memory, respectively, to store calculations, which is well within the limits of existing classical computers.

This is an excellent paper, which sets a new record for the classical simulation of generic quantum circuits; I congratulate the authors for it.  Now, though, I want you to take a deep breath and repeat after me:

This paper does not undercut the rationale for quantum supremacy experiments.  The truth, ironically, is almost the opposite: it being possible to simulate 49-qubit circuits using a classical computer is a precondition for Google’s planned quantum supremacy experiment, because it’s the only way we know to check such an experiment’s results!  The goal, with sampling-based quantum supremacy, was always to target the “sweet spot,” which we estimated at around 50 qubits, where classical simulation is still possible, but it’s clearly orders of magnitude more expensive than doing the experiment itself.  If you like, the goal is to get as far as you can up the mountain of exponentiality, conditioned on people still being able to see you from the base.  Why?  Because you can.  Because it’s there.  Because it challenges those who think quantum computing will never scale: explain this, punks!  But there’s no point unless you can verify the result.

Related to that, the paper does not refute any prediction I made, by doing anything I claimed was impossible.  On the contrary (if you must know), the paper confirms something that I predicted would be possible.  People said: “40 qubits is the practical limit of what you can simulate, so there’s no point in Google or anyone else doing a supremacy experiment with 49 qubits, since they can never verify the results.”  I would shrug and say something like: “eh, if you can do 40 qubits, then I’m sure you can do 50.  It’s only a thousand times harder!”

So, how does the paper get up to 50 qubits?  A lot of computing power and a lot of clever tricks, one of which (the irony thickens…) came from a paper that I recently coauthored with Lijie Chen: Complexity-Theoretic Foundations of Quantum Supremacy Experiments.  Lijie and I were interested in the question: what’s the best way to simulate a quantum circuit with n qubits and m gates?  We noticed that there’s a time/space tradeoff here: you could just store the entire amplitude vector in memory and update, which would take exp(n) memory but also “only” about exp(n) time.  Or you could compute the amplitudes you cared about via Feynman sums (as in the proof of BQP⊆PSPACE), which takes only linear memory, but exp(m) time.  If you imagine, let’s say, n=50 and m=1000, then exp(n) might be practical if you’re IBM or Google, but exp(m) is certainly not.

So then we raised the question: could one get the best of both worlds?  That is, could one simulate such a quantum circuit using both linear memory and exp(n) time?  And we showed that this is almost possible: we gave an algorithm that uses linear memory and dO(n) time, where d is the circuit depth.  Furthermore, the more memory it has available, the faster our algorithm will run—until, in the limit of exponential memory, it just becomes the “store the whole amplitude vector” algorithm mentioned above.  I’m not sure why this algorithm wasn’t discovered earlier, especially since it basically just amounts to Savitch’s Theorem from complexity theory.  In any case, though, the IBM group used this idea among others to take full advantage of the RAM it had available.

Let me make one final remark: this little episode perfectly illustrates why theoretical computer scientists like to talk about polynomial vs. exponential rather than specific numbers.  If you keep your eyes on the asymptotic fundamentals, rather than every factor of 10 or 1000, then you’re not constantly shocked by events, like a dog turning its head for every passing squirrel.  Before you could simulate 40 qubits, now you can simulate 50.  Maybe with more cleverness you could get to 60 or even 70.  But … dude.  The problem is still exponential time.

We saw the same “SQUIRREL!  SQUIRREL!” reaction with the people who claimed that the wonderful paper by Clifford and Clifford had undercut the rationale for BosonSampling experiments, by showing how to solve the problem in “merely” ~2n time rather than ~mn, where n is the number of photons and m is the number of modes.  Of course, Arkhipov and I had never claimed more than ~2n hardness for the problem, and Clifford and Clifford’s important result had justified our conservatism on that point, but, y’know … SQUIRREL!

More broadly, it seems to me that this dynamic constantly occurs in the applied cryptography world.  OMIGOD a 128-bit hash function has been broken!  Big news!  OMIGOD a new, harder hash function has been designed!  Bigger news!  OMIGOD OMIGOD OMIGOD the new one was broken too!!  All of it fully predictable once you realize that we’re on the shores of an exponentially hard problem, and for some reason, refusing to go far enough out into the sea (i.e., pick large enough security parameters) that none of this back-and-forth would happen.

I apologize, sincerely, if I come off as too testy in this post.  No doubt it’s entirely the fault of a cognitive defect on my end, wherein ten separate people asking me about something get treated by my brain like a single person who still doesn’t get it even after I’ve explained it ten times.

Because you asked: the Simulation Hypothesis has not been falsified; remains unfalsifiable

Tuesday, October 3rd, 2017

By email, by Twitter, even as the world was convulsed by tragedy, the inquiries poured in yesterday about a different topic entirely: Scott, did physicists really just prove that the universe is not a computer simulation—that we can’t be living in the Matrix?

What prompted this was a rash of popular articles like this one (“Researchers claim to have found proof we are NOT living in a simulation”).  The articles were all spurred by a recent paper in Science Advances: Quantized gravitational responses, the sign problem, and quantum complexity, by Zohar Ringel of Hebrew University and Dmitry L. Kovrizhin of Oxford.

I’ll tell you what: before I comment, why don’t I just paste the paper’s abstract here.  I invite you to read it—not the whole paper, just the abstract, but paying special attention to the sentences—and then make up your own mind about whether it supports the interpretation that all the popular articles put on it.

Ready?  Set?

Abstract: It is believed that not all quantum systems can be simulated efficiently using classical computational resources.  This notion is supported by the fact that it is not known how to express the partition function in a sign-free manner in quantum Monte Carlo (QMC) simulations for a large number of important problems.  The answer to the question—whether there is a fundamental obstruction to such a sign-free representation in generic quantum systems—remains unclear.  Focusing on systems with bosonic degrees of freedom, we show that quantized gravitational responses appear as obstructions to local sign-free QMC.  In condensed matter physics settings, these responses, such as thermal Hall conductance, are associated with fractional quantum Hall effects.  We show that similar arguments also hold in the case of spontaneously broken time-reversal (TR) symmetry such as in the chiral phase of a perturbed quantum Kagome antiferromagnet.  The connection between quantized gravitational responses and the sign problem is also manifested in certain vertex models, where TR symmetry is preserved.

For those tuning in from home, the “sign problem” is an issue that arises when, for example, you’re trying to use the clever trick known as Quantum Monte Carlo (QMC) to learn about the ground state of a quantum system using a classical computer—but where you needed probabilities, which are real numbers from 0 to 1, your procedure instead spits out numbers some of which are negative, and which you can therefore no longer use to define a sensible sampling process.  (In some sense, it’s no surprise that this would happen when you’re trying to simulate quantum mechanics, which of course is all about generalizing the rules of probability in a way that involves negative and even complex numbers!  The surprise, rather, is that QMC lets you avoid the sign problem as often as it does.)

Anyway, this is all somewhat far from my expertise, but insofar as I understand the paper, it looks like a serious contribution to our understanding of the sign problem, and why local changes of basis can fail to get rid of it when QMC is used to simulate certain bosonic systems.  It will surely interest QMC experts.

OK, but does any of this prove that the universe isn’t a computer simulation, as the popular articles claim (and as the original paper does not)?

It seems to me that, to get from here to there, you’d need to overcome four huge difficulties, any one of which would be fatal by itself, and which are logically independent of each other.

  1. As a computer scientist, one thing that leapt out at me, is that Ringel and Kovrizhin’s paper is fundamentally about computational complexity—specifically, about which quantum systems can and can’t be simulated in polynomial time on a classical computer—yet it’s entirely innocent of the language and tools of complexity theory.  There’s no BQP, no QMA, no reduction-based hardness argument anywhere in sight, and no clearly-formulated request for one either.  Instead, everything is phrased in terms of the failure of one specific algorithmic framework (namely QMC)—and within that framework, only “local” transformations of the physical degrees of freedom are considered, not nonlocal ones that could still be accessible to polynomial-time algorithms.  Of course, one does whatever one needs to do to get a result.
    To their credit, the authors do seem aware that a language for discussing all possible efficient algorithms exists.  They write, for example, of a “common understanding related to computational complexity classes” that some quantum systems are hard to simulate, and specifically of the existence of systems that support universal quantum computation.  So rather than criticize the authors for this limitation of their work, I view their paper as a welcome invitation for closer collaboration between the quantum complexity theory and quantum Monte Carlo communities, which approach many of the same questions from extremely different angles.  As official ambassador between the two communities, I nominate Matt Hastings.
  2. OK, but even if the paper did address computational complexity head-on, about the most it could’ve said is that computer scientists generally believe that BPP≠BQP (i.e., that quantum computers can solve more decision problems in polynomial time than classical probabilistic ones); and that such separations are provable in the query complexity and communication complexity worlds; and that at any rate, quantum computers can solve exact sampling problems that are classically hard unless the polynomial hierarchy collapses (as pointed out in the BosonSampling paper, and independently by Bremner, Jozsa, Shepherd).  Alas, until someone proves P≠PSPACE, there’s no hope for an unconditional proof that quantum computers can’t be efficiently simulated by classical ones.
    (Incidentally, the paper comments, “Establishing an obstruction to a classical simulation is a rather ill-defined task.”  I beg to differ: it’s not ill-defined; it’s just ridiculously hard!)
  3. OK, but suppose it were proved that BPP≠BQP—and for good measure, suppose it were also experimentally demonstrated that scalable quantum computing is possible in our universe.  Even then, one still wouldn’t by any stretch have ruled out that the universe was a computer simulation!  For as many of the people who emailed me asked themselves (but as the popular articles did not), why not just imagine that the universe is being simulated on a quantum computer?  Like, duh?
  4. Finally: even if, for some reason, we disallowed using a quantum computer to simulate the universe, that still wouldn’t rule out the simulation hypothesis.  For why couldn’t God, using Her classical computer, spend a trillion years to simulate one second as subjectively perceived by us?  After all, what is exponential time to She for whom all eternity is but an eyeblink?

Anyway, if it weren’t for all four separate points above, then sure, physicists would have now proved that we don’t live in the Matrix.

I do have a few questions of my own, for anyone who came here looking for my reaction to the ‘news’: did you really need me to tell you all this?  How much of it would you have figured out on your own, just by comparing the headlines of the popular articles to the descriptions (however garbled) of what was actually done?  How obvious does something need to be, before it no longer requires an ‘expert’ to certify it as such?  If I write 500 posts like this one, will the 501st post basically just write itself?

Asking for a friend.


Comment Policy: I welcome discussion about the Ringel-Dovrizhin paper; what might’ve gone wrong with its popularization; QMC; the sign problem; the computational complexity of condensed-matter problems more generally; and the relevance or irrelevance of work on these topics to broader questions about the simulability of the universe.  But as a little experiment in blog moderation, I won’t allow comments that just philosophize in general about whether or not the universe is a simulation, without making further contact with the actual content of this post.  We’ve already had the latter conversation here—probably, like, every week for the last decade—and I’m ready for something new.

GapP, Oracles, and Quantum Supremacy

Friday, September 1st, 2017

Let me start with a few quick announcements before the main entrée:

First, the website haspvsnpbeensolved.com is now live!  Thanks so much to my friend Adam Chalmers for setting it up.  Please try it out on your favorite P vs. NP solution paper—I think you’ll be impressed by how well our secret validation algorithm performs.

Second, some readers might enjoy a YouTube video of me lecturing about the computability theory of closed timelike curves, from the Workshop on Computational Complexity and High Energy Physics at the University of Maryland a month ago.  Other videos from the workshop—including of talks by John Preskill, Daniel Harlow, Stephen Jordan, and other names known around Shtetl-Optimized, and of a panel discussion in which I participated—are worth checking out as well.  Thanks so much to Stephen for organizing such a great workshop!

Third, thanks to everyone who’s emailed to ask whether I’m holding up OK with Hurricane Harvey, and whether I know how to swim (I do).  As it happens, I haven’t been in Texas for two months—I spent most of the summer visiting NYU and doing other travel, and this year, Dana and I are doing an early sabbatical at Tel Aviv University.  However, I understand from friends that Austin, being several hours’ drive further inland, got nothing compared to what Houston did, and that UT is open on schedule for the fall semester.  Hopefully our house is still standing as well!  Our thoughts go to all those affected by the disaster in Houston.  Eventually, the Earth’s rapidly destabilizing climate almost certainly means that Austin will be threatened as well by “500-year events” happening every year or two, as for that matter will a large portion of the earth’s surface.  For now, though, Austin lives to be weird another day.


GapP, Oracles, and Quantum Supremacy

by Scott Aaronson

Stuart Kurtz 60th Birthday Conference, Columbia, South Carolina

August 20, 2017

It’s great to be here, to celebrate the life and work of Stuart Kurtz, which could never be … eclipsed … by anything.

I wanted to say something about work in structural complexity and counting complexity and oracles that Stuart was involved with “back in the day,” and how that work plays a major role in issues that concern us right now in quantum computing.  A major goal for the next few years is the unfortunately-named Quantum Supremacy.  What this means is to get a clear quantum speedup, for some task: not necessarily a useful task, but something that we can be as confident as possible is classically hard.  For example, consider the 49-qubit superconducting chip that Google is planning to fabricate within the next year or so.  This won’t yet be good enough for running Shor’s algorithm, to factor numbers of any interesting size, but it hopefully will be good enough to sample from a probability distribution over n-bit strings—in this case, 49-bit strings—that’s hard to sample from classically, taking somewhere on the order of 249 steps.

Furthermore, the evidence that that sort of thing is indeed classically hard, might actually be stronger than the evidence that factoring is classically hard.  As I like to say, a fast classical factoring algorithm would “merely” collapse the world’s electronic commerce—as far as we know, it wouldn’t collapse the polynomial hierarchy!  By contrast, a fast classical algorithm to simulate quantum sampling would collapse the polynomial hierarchy, assuming the simulation is exact.  Let me first go over the argument for that, and then explain some of the more recent things we’ve learned.

Our starting point will be two fundamental complexity classes, #P and GapP.

#P is the class of all nonnegative integer functions f, for which there exists a nondeterministic polynomial-time Turing machine M such that f(x) equals the number of accepting paths of M(x).  Less formally, #P is the class of problems that boil down to summing up an exponential number of nonnegative terms, each of which is efficiently computable individually.

GapP—introduced by Fenner, Fortnow, and Kurtz in 1992—can be defined as the set {f-g : f,g∈#P}; that is, the closure of #P under subtraction.  Equivalently, GapP is the class of problems that boil down to summing up an exponential number of terms, each of which is efficiently computable individually, but which could be either positive or negative, and which can therefore cancel each other out.  As you can see, GapP is a class that in some sense anticipates quantum computing!

For our purposes, the most important difference between #P and GapP is that #P functions can at least be multiplicatively approximated in the class BPPNP, by using Stockmeyer’s technique of approximating counting with universal hash functions.  By contrast, even if you just want to approximate a GapP function to within (say) a factor of 2—or for that matter, just decide whether a GapP function is positive or negative—it’s not hard to see that that’s already a #P-hard problem.  For, supposing we had an oracle to solve this problem, we could then shift the sum this way and that by adding positive and negative dummy terms, and use binary search, to zero in on the sum’s exact value in polynomial time.

It’s also not hard to see that a quantum computation can encode an arbitrary GapP function in one of its amplitudes.  Indeed, let s:{0,1}n→{1,-1} be any Boolean function that’s given by a polynomial-size circuit.  Then consider the quantum circuit below.

When we run this circuit, the probability that we see the all-0 string as output is

$$ \left( \frac{1}{\sqrt{2^n}} \sum_{z\in \{0,1\}^n} s(z) \right)^2 = \frac{1}{2^n} \sum_{z,w\in \{0,1\}^n} s(z) s(w) $$

which is clearly in GapP, and clearly #P-hard even to approximate to within a multiplicative factor.

By contrast, suppose we had a probabilistic polynomial-time classical algorithm, call it M, to sample the output distribution of the above quantum circuit.  Then we could rewrite the above probability as Prr[M(r) outputs 0…0], where r consists of the classical random bits used by M.  This is again an exponentially large sum, with one term for each possible r value—but now it’s a sum of nonnegative terms (probabilities), which is therefore approximable in BPPNP.

We can state the upshot as follows.  Let ExactSampBPP be the class of sampling problems—that is, families of probability distributions {Dx}x, one for each input x∈{0,1}n—for which there exists a polynomial-time randomized algorithm that outputs a sample exactly from Dx, in time polynomial in |x|.  Let ExactSampBQP be the same thing except that we allow a polynomial-time quantum algorithm.  Then we have that, if ExactSampBPP = ExactSampBQP, then squared sums of both positive and negative terms, could efficiently be rewritten as sums of nonnegative terms only—and hence P#P=BPPNP.  This, in turn, would collapse the polynomial hierarchy to the third level, by Toda’s Theorem that PH⊆P#P, together with the result BPPNP⊆∑3.  To summarize:

Theorem 1.  Quantum computers can efficiently solve exact sampling problems that are classically hard unless the polynomial hierarchy collapses.

(In fact, the argument works not only if the classical algorithm exactly samples Dx, but if it samples from any distribution in which the probabilities are multiplicatively close to Dx‘s.  If we really only care about exact sampling, then we can strengthen the conclusion to get that PH collapses to the second level.)

This sort of reasoning was implicit in several early works, including those of Fenner et al. and Terhal and DiVincenzo.  It was made fully explicit in my paper with Alex Arkhipov on BosonSampling in 2011, and in the independent work of Bremner, Jozsa, and Shepherd on the IQP model.  These works actually showed something stronger, which is that we get a collapse of PH, not merely from a fast classical algorithm to simulate arbitrary quantum systems, but from fast classical algorithms to simulate various special quantum systems.  In the case of BosonSampling, that special system is a collection of identical, non-interacting photons passing through a network of beamsplitters, then being measured at the very end to count the number of photons in each mode.  In the case of IQP, the special system is a collection of qubits that are prepared, subjected to some commuting Hamiltonians acting on various subsets of the qubits, and then measured.  These special systems don’t seem to be capable of universal quantum computation (or for that matter, even universal classical computation!)—and correspondingly, many of them seem easier to realize in the lab than a full universal quantum computer.

From an experimental standpoint, though, all these results are unsatisfactory, because they all talk only about the classical hardness of exact (or very nearly exact) sampling—and indeed, the arguments are based around the hardness of estimating just a single, exponentially-small amplitude.  But any real experiment will have tons of noise and inaccuracy, so it seems only fair to let the classical simulation be subject to serious noise and inaccuracy as well—but as soon as we do, the previous argument collapses.

Thus, from the very beginning, Alex Arkhipov and I took it as our “real” goal to show, under some reasonable assumption, that there’s a distribution D that a polynomial-time quantum algorithm can sample from, but such that no polynomial-time classical algorithm can sample from any distribution that’s even ε-close to D in variation distance.  Indeed, this goal is what led us to BosonSampling in the first place: we knew that we needed amplitudes that were not only #P-hard but “robustly” #P-hard; we knew that the permanent of an n×n matrix (at least over finite fields) was the canonical example of a “robustly” #P-hard function; and finally, we knew that systems of identical non-interacting bosons, such as photons, gave rise to amplitudes that were permanents in an extremely natural way.  The fact that photons actually exist in the physical world, and that our friends with quantum optics labs like to do experiments with them, was just a nice bonus!

A bit more formally, let ApproxSampBPP be the class of sampling problems for which there exists a classical algorithm that, given an input x∈{0,1}n and a parameter ε>0, samples a distribution that’s at most  away from Dx in variation distance, in time polynomial in n and 1/ε.  Let ApproxSampBQP be the same except that we allow a quantum algorithm.  Then the “dream” result that we’d love to prove—both then and now—is the following.

Strong Quantum Supremacy Conjecture.  If ApproxSampBPP = ApproxSampBQP, then the polynomial hierarchy collapses.

Unfortunately, Alex and I were only able to prove this conjecture assuming a further hypothesis, about the permanents of i.i.d. Gaussian matrices.

Theorem 2 (A.-Arkhipov).  Given an n×n matrix X of independent complex Gaussian entries, each of mean 0 and variance 1, assume it’s a #P-hard problem to approximate |Per(X)|2 to within ±ε⋅n!, with probability at least 1-δ over the choice of X, in time polynomial in n, 1/ε, and 1/δ.  Then the Strong Quantum Supremacy Conjecture holds.  Indeed, more than that: in such a case, even a fast approximate classical simulation of BosonSampling, in particular, would imply P#P=BPPNP and hence a collapse of PH.

Alas, after some months of effort, we were unable to prove the needed #P-hardness result for Gaussian permanents, and it remains an outstanding open problem—there’s not even a consensus as to whether it should be true or false.  Note that there is a famous polynomial-time classical algorithm to approximate the permanents of nonnegative matrices, due to Jerrum, Sinclair, and Vigoda, but that algorithm breaks down for matrices with negative or complex entries.  This is once again the power of cancellations, the difference between #P and GapP.

Frustratingly, if we want the exact permanents of i.i.d. Gaussian matrices, we were able to prove that that’s #P-hard; and if we want the approximate permanents of arbitrary matrices, we also know that that’s #P-hard—it’s only when we have approximation and random inputs in the same problem that we no longer have the tools to prove #P-hardness.

In the meantime, one can also ask a meta-question.  How hard should it be to prove the Strong Quantum Supremacy Conjecture?  Were we right to look at slightly exotic objects, like the permanents of Gaussian matrices?  Or could Strong Quantum Supremacy have a “pure, abstract complexity theory proof”?

Well, one way to formalize that question is to ask whether Strong Quantum Supremacy has a relativizing proof, a proof that holds in the presence of an arbitrary oracle.  Alex and I explicitly raised that as an open problem in our BosonSampling paper.

Note that “weak” quantum supremacy—i.e., the statement that ExactSampBPP = ExactSampBQP collapses the polynomial hierarchy—has a relativizing proof, namely the proof that I sketched earlier.  All the ingredients that we used—Toda’s Theorem, Stockmeyer approximate counting, simple manipulations of quantum circuits—were relativizing ingredients.  By contrast, all the way back in 1998, Fortnow and Rogers proved the following.

Theorem 3 (Fortnow and Rogers).  There exists an oracle relative to which P=BQP and yet PH is infinite.

In other words, if you want to prove that P=BQP collapses the polynomial hierarchy, the proof can’t be relativizing.  This theorem was subsequently generalized in a paper by Fenner, Fortnow, Kurtz, and Li, which used concepts like “generic oracles” that seem powerful but that I don’t understand.

The trouble is, Fortnow and Rogers’s construction was extremely tailored to making P=BQP.  It didn’t even make PromiseBPP=PromiseBQP (that is, it allowed that quantum computers might still be stronger than classical ones for promise problems), let alone did it collapse quantum with classical for sampling problems.

We can organize the various quantum/classical collapse possibilities as follows:

ExactSampBPP = ExactSampBQP

ApproxSampBPP = ApproxSampBQP   ⇔   FBPP = FBQP

PromiseBPP = PromiseBQP

BPP = BQP

Here FBPP is the class of relation problems solvable in randomized polynomial time—that is, problems where given an input x∈{0,1}n and a parameter ε>0, the goal is to produce any output in a certain set Sx, with success probability at least 1-ε, in time polynomial in n and 1/ε.  FBQP is the same thing except for quantum polynomial time.

The equivalence between the two equalities ApproxSampBPP = ApproxSampBQP and FBPP=FBQP is not obvious, and was the main result in my 2011 paper The Equivalence of Sampling and Searching.  While it’s easy to see that ApproxSampBPP = ApproxSampBQP implies FBPP=FBQP, the opposite direction requires us to take an arbitrary sampling problem S, and define a relation problem RS that has “essentially the same difficulty” as S (in the sense that RS has an efficient classical algorithm iff S does, RS has an efficient quantum algorithm iff S does, etc).  This, in turn, we do using Kolmogorov complexity: basically, RS asks us to output a tuple of samples that have large probabilities according to the requisite probability distribution from the sampling problem; and that also, conditioned on that, are close to algorithmically random.  The key observation is that, if a probabilistic Turing machine of fixed size can solve that relation problem for arbitrarily large inputs, then it must be doing so by sampling from a probability distribution close in variation distance to D—since any other approach would lead to outputs that were algorithmically compressible.

Be that as it may, staring at the chain of implications above, a natural question is which equalities in the chain collapse the polynomial hierarchy in a relativizing way, and which equalities collapse PH (if they do) only for deeper, non-relativizing reasons.

This is one of the questions that Lijie Chen and I took up, and settled, in our paper Complexity-Theoretic Foundations of Quantum Supremacy Experiments, which was presented at this summer’s Computational Complexity Conference (CCC) in Riga.  The “main” results in our paper—or at least, the results that the physicists care about—were about how confident we can be in the classical hardness of simulating quantum sampling experiments with random circuits, such as the experiments that the Google group will hopefully be able to do with its 49-qubit device in the near future.  This involved coming up with a new hardness assumption, which was tailored to those sorts of experiments, and giving a reduction from that new assumption, and studying how far existing algorithms come toward breaking the new assumption (tl;dr: not very far).

But our paper also had what I think of as a “back end,” containing results mainly of interest to complexity theorists, about what kinds of quantum supremacy theorems we can and can’t hope for in principle.  When I’m giving talks about our paper to physicists, I never have time to get to this back end—it’s always just “blah, blah, we also did some stuff involving structural complexity and oracles.”  But given that a large fraction of all the people on earth who enjoy those things are probably right here in this room, in the rest of this talk, I’d like to tell you about what was in the back end.

The first thing there was the following result.

Theorem 4 (A.-Chen).  There exists an oracle relative to which ApproxSampBPP = ApproxSampBQP and yet PH is infinite. In other words, any proof of the Strong Quantum Supremacy Conjecture will require non-relativizing techniques.

Theorem 4 represents a substantial generalization of Fortnow and Rogers’s Theorem 3, in that it makes quantum and classical equivalent not only for promise problems, but even for approximate sampling problems.  There’s also a sense in which Theorem 4 is the best possible: as we already saw, there are no oracles relative to which ExactSampBPP = ExactSampBQP and yet PH is infinite, because the opposite conclusion relativizes.

So how did we prove Theorem 4?  Well, we learned at this workshop that Stuart Kurtz pioneered the development of principled ways to prove oracle results just like this one, with multiple “nearly conflicting” requirements.  But, because we didn’t know that at the time, we basically just plunged in and built the oracle we wanted by hand!

In more detail, you can think of our oracle construction as proceeding in three steps.

  1. We throw in an oracle for a PSPACE-complete problem.  This collapses ApproxSampBPP with ApproxSampBQP, which is what we want.  Unfortunately, it also collapses the polynomial hierarchy down to P, which is not what we want!
  2. So then we need to add in a second part of the oracle that makes PH infinite again.  From Håstad’s seminal work in the 1980s until recently, even if we just wanted any oracle that makes PH infinite, without doing anything else at the same time, we only knew how to achieve that with quite special oracles.  But in their 2015 breakthrough, Rossman, Servedio, and Tan have shown that even a random oracle makes PH infinite with probability 1.  So for simplicity, we might as well take this second part of the oracle to be random.  The “only” problem is that, along with making PH infinite, a random oracle will also re-separate ApproxSampBPP and ApproxSampBQP (and for that matter, even ExactSampBPP and ExactSampBQP)—for example, because of the Fourier sampling task performed by the quantum circuit I showed you earlier!  So we once again seem back where we started.
    (To ward off confusion: ever since Fortnow and Rogers posed the problem in 1998, it remains frustratingly open whether BPP and BQP can be separated by a random oracle—that’s a problem that I and others have worked on, making partial progress that makes a query complexity separation look unlikely without definitively ruling one out.  But separating the sampling versions of BPP and BQP by a random oracle is much, much easier.)
  3. So, finally, we need to take the random oracle that makes PH infinite, and “scatter its bits around randomly” in such a way that a PH machine can still find the bits, but an ApproxSampBQP machine can’t.  In other words: given our initial random oracle A, we can make a new oracle B such that B(y,r)=(1,A(y)) if r is equal to a single randomly-chosen “password” ry, depending on the query y, and B(y,r)=(0,0) otherwise.  In that case, it takes just one more existential quantifier to guess the password ry, so PH can do it, but a quantum algorithm is stuck, basically because the linearity of quantum mechanics makes the algorithm not very sensitive to tiny random changes to the oracle string (i.e., the same reason why Grover’s algorithm can’t be arbitrarily sped up).  Incidentally, the reason why the password ry needs to depend on the query y is that otherwise the input x to the quantum algorithm could hardcode a password, and thereby reveal exponentially many bits of the random oracle A.

We should now check: why does the above oracle “only” collapse ApproxSampBPP and ApproxSampBQP?  Why doesn’t it also collapse ExactSampBPP and ExactSampBQP—as we know that it can’t, by our previous argument?  The answer is: because a quantum algorithm does have an exponentially small probability of correctly guessing a given password ry.  And that’s enough to make the distribution sampled by the quantum algorithm differ, by 1/exp(n) in variation distance, from the distribution sampled by any efficient classical simulation of the algorithm—an error that doesn’t matter for approximate sampling, but does matter for exact sampling.

Anyway, it’s then just like seven pages of formalizing the above intuitions and you’re done!

OK, since there seems to be time, I’d like to tell you about one more result from the back end of my and Lijie’s paper.

If we can work relative to whatever oracle A we like, then it’s easy to get quantum supremacy, and indeed BPPA≠BQPA.  We can, for example, use Simon’s problem, or Shor’s period-finding problem, or Forrelation, or other choices of black-box problems that admit huge, provable quantum speedups.  In the unrelativized world, by contrast, it’s clear that we have to make some complexity assumption for quantum supremacy—even if we just want ExactSampBPP ≠ ExactSampBQP.  For if (say) P=P#P, then ExactSampBPP and ExactSampBQP would collapse as well.

Lijie and I were wondering: what happens if we try to “interpolate” between the relativized and unrelativized worlds?  More specifically, what happens if our algorithms are allowed to query a black box, but we’re promised that whatever’s inside the black box is efficiently computable (i.e., has a small circuit)?  How hard is it to separate BPP from BQP, or ApproxSampBPP from ApproxSampBQP, relative to an oracle A that’s constrained to lie in P/poly?

Here, we’ll start with a beautiful observation that’s implicit in 2004 work by Servedio and Gortler, as well as 2012 work by Mark Zhandry.  In our formulation, this observation is as follows:

Theorem 5.  Suppose there exist cryptographic one-way functions (even just against classical adversaries).  Then there exists an oracle A∈P/poly such that BPPA≠BQPA.

While we still need to make a computational hardness assumption here, to separate quantum from classical computing, the surprise is that the assumption is so much weaker than what we’re used to.  We don’t need to assume the hardness of factoring or discrete log—or for that matter, of any “structured” problem that could be a basis for, e.g., public-key cryptography.  Just a one-way function that’s hard to invert, that’s all!

The intuition here is really simple.  Suppose there’s a one-way function; then it’s well-known, by the HILL and GGM Theorems of classical cryptography, that we can bootstrap it to get a cryptographic pseudorandom function family.  This is a family of polynomial-time computable functions fs:{0,1}n→{0,1}n, parameterized by a secret seed s, such that fs can’t be distinguished from a truly random function f by any polynomial-time algorithm that’s given oracle access to the function and that doesn’t know s.  Then, as our efficiently computable oracle A that separates quantum from classical computing, we take an ensemble of functions like

gs,r(x) = fs(x mod r),

where r is an exponentially large integer that serves as a “hidden period,” and s and r are both secrets stored by the oracle that are inaccessible to the algorithm that queries it.

The reasoning is now as follows: certainly there’s an efficient quantum algorithm to find r, or to solve some decision problem involving r, which we can use to define a language that’s in BQPA but not in BPPA.  That algorithm is just Shor’s period-finding algorithm!  (Technically, Shor’s algorithm needs certain assumptions on the starting function fs to work—e.g., it couldn’t be a constant function—but if those assumptions aren’t satisfied, then fs wasn’t pseudorandom anyway.)  On the other hand, suppose there were an efficient classical algorithm to find the period r.  In that case, we have a dilemma on our hands: would the classical algorithm still have worked, had we replaced fs by a truly random function?  If so, then the classical algorithm would violate well-known lower bounds on the classical query complexity of period-finding.  But if not, then by working on pseudorandom functions but not on truly random functions, the algorithm would be distinguishing the two—so fs wouldn’t have been a cryptographic pseudorandom function at all, contrary to assumption!

This all caused Lijie and me to wonder whether Theorem 5 could be strengthened even further, so that it wouldn’t use any complexity assumption at all.  In other words, why couldn’t we just prove unconditionally that there’s an oracle A∈P/poly such that BPPA≠BQPA?  By comparison, it’s not hard to see that we can unconditionally construct an oracle A∈P/poly such that PA≠NPA.

Alas, with the following theorem, we were able to explain why BPP vs. BQP (and even ApproxSampBPP vs. ApproxSampBQP) are different, and why some computational assumption is still needed to separate quantum from classical, even if we’re working relative to an efficiently computable oracle.

Theorem 6 (A.-Chen).  Suppose that, in the real world, ApproxSampBPP = ApproxSampBQP and NP⊆BPP (granted, these are big assumptions!).  Then ApproxSampBPPA = ApproxSampBQPA for all oracles A∈P/poly.

Taking the contrapositive, this is saying that you can’t separate ApproxSampBPP from ApproxSampBQP relative to an efficiently computable oracle, without separating some complexity classes in the real world.  This contrasts not only with P vs. NP, but even with ExactSampBPP vs. ExactSampBQP, which can be separated unconditionally relative to efficiently computable oracles.

The proof of Theorem 6 is intuitive and appealing.  Not surprisingly, we’re going to heavily exploit the assumptions ApproxSampBPP = ApproxSampBQP and NP⊆BPP.  Let Q be a polynomial-time quantum algorithm that queries an oracle A∈P/poly.  Then we need to simulate Q—and in particular, sample close to the same probability distribution over outputs—using a polynomial-time classical algorithm that queries A.

Let

$$ \sum_{x,w} \alpha_{x,w} \left|x,w\right\rangle $$

be the state of Q immediately before its first query to the oracle A, where x is the input to be submitted to the oracle.  Then our first task is to get a bunch of samples from the probability distribution D={|αx,w|2}x,w, or something close to D in variation distance.  But this is easy to do, using the assumption ApproxSampBPP = ApproxSampBQP.

Let x1,…,xk be our samples from D, marginalized to the x part.  Then next, our classical algorithm queries A on each of x1,…,xk, getting responses A(x1),…,A(xk).  The next step is to search for a function f∈P/poly—or more specifically, a function of whatever fixed polynomial size is relevant—that agrees with A on the sample data, i.e. such that f(xi)=A(xi) for all i∈[k].  This is where we’ll use the assumption NP⊆BPP (together, of course, with the fact that at least one such f exists, namely A itself!), to make the task of finding f efficient.  We’ll also appeal to a fundamental fact about the sample complexity of PAC-learning.  The fact is that, if we find a polynomial-size circuit f that agrees with A on a bunch of sample points drawn independently from a distribution, then f will probably agree with A on most further points drawn from the same distribution as well.

So, OK, we then have a pretty good “mock oracle,” f, that we can substitute for the real oracle on the first query that Q makes.  Of course f and A won’t perfectly agree, but the small fraction of disagreements won’t matter much, again because of the linearity of quantum mechanics (i.e., the same thing that prevents us from speeding up Grover’s algorithm arbitrarily).  So we can basically simulate Q’s first query, and now our classical simulation is good to go until Q’s second query!  But now you can see where this is going: we iterate the same approach, and reuse the same assumptions ApproxSampBPP = ApproxSampBQP and NP⊆BPP, to find a new “mock oracle” that lets us simulate Q’s second query, and so on until all of Q’s queries have been simulated.

OK, I’ll stop there.  I don’t have a clever conclusion or anything.  Thank you.