## Archive for the ‘Speaking Truth to Parallelism’ 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!

Update (Feb. 21): For casual readers, Vivek Wadhwa quickly came into the comments section to try to defend himself—before leaving in a huff as a chorus of commenters tried to explain why he was wrong. As far as I know, he has not posted any corrections to his Washington Post piece. Wadhwa’s central defense was that he was simply repeating what Michelle Simmons, a noted quantum computing experimentalist in Australia, said in various talks in YouTube—which turns out to be largely true (though Wadhwa said explicitly that quantum computers could efficiently solve TSP, while Simmons mostly left this as an unstated implication). As a result, while Wadhwa should obviously have followed the journalistic practice of checking incredible-sounding claims—on Wikipedia if nowhere else!—before repeating them in the Washington Post, I now feel that Simmons shares in the responsibility for this. As John Preskill tweeted, an excellent lesson to draw from this affair is that everyone in our field needs to be careful to say things that are true when speaking to the public.

### 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? ### Insert D-Wave Post Here Thursday, March 16th, 2017 In the two months since I last blogged, the US has continued its descent into madness. Yet even while so many certainties have proven ephemeral as the morning dew—the US’s autonomy from Russia, the sanity of our nuclear chain of command, the outcome of our Civil War, the constraints on rulers that supposedly set us apart from the world’s dictator-run hellholes—I’ve learned that certain facts of life remain constant. The moon still waxes and wanes. Electrons remain bound to their nuclei. P≠NP proofs still fill my inbox. Squirrels still gather acorns. And—of course!—people continue to claim big quantum speedups using D-Wave devices, and those claims still require careful scrutiny. With that preamble, I hereby offer you eight quantum computing news items. Cathy McGeoch Episode II: The Selby Comparison On January 17, a group from D-Wave—including Cathy McGeoch, who now works directly for D-Wave—put out a preprint claiming a factor-of-2500 speedup for the D-Wave machine (the new, 2000-qubit one) compared to the best classical algorithms. Notably, they wrote that the speedup persisted when they compared against simulated annealing, quantum Monte Carlo, and even the so-called Hamze-de Freitas-Selby (HFS) algorithm, which was often the classical victor in previous performance comparisons against the D-Wave machine. Reading this, I was happy to see how far the discussion has advanced since 2013, when McGeoch and Cong Wang reported a factor-of-3600 speedup for the D-Wave machine, but then it turned out that they’d compared only against classical exact solvers rather than heuristics—a choice for which they were heavily criticized on this blog and elsewhere. (And indeed, that particular speedup disappeared once the classical computer’s shackles were removed.) So, when people asked me this January about the new speedup claim—the one even against the HFS algorithm—I replied that, even though we’ve by now been around this carousel several times, I felt like the ball was now firmly in the D-Wave skeptics’ court, to reproduce the observed performance classically. And if, after a year or so, no one could, that would be a good time to start taking seriously that a D-Wave speedup might finally be here to stay—and to move on to the next question, of whether this speedup had anything to do with quantum computation, or only with the building of a piece of special-purpose optimization hardware. A&M: Annealing and Matching As it happened, it only took one month. On March 2, Salvatore Mandrà, Helmut Katzgraber, and Creighton Thomas put up a response preprint, pointing out that the instances studied by the D-Wave group in their most recent comparison are actually reducible to the minimum-weight perfect matching problem—and for that reason, are solvable in polynomial time on a classical computer. Much of Mandrà et al.’s paper just consists of graphs, wherein they plot the running times of the D-Wave machine and of a classical heuristic on the relevant instances—clearly all different flavors of exponential—and then Edmonds’ matching algorithm from the 1960s, which breaks away from the pack into polynomiality. But let me bend over backwards to tell you the full story. Last week, I had the privilege of visiting Texas A&M to give a talk. While there, I got to meet Helmut Katzgraber, a condensed-matter physicist who’s one of the world experts on quantum annealing experiments, to talk to him about their new response paper. Helmut was clear in his prediction that, with only small modifications to the instances considered, one could see similar performance by the D-Wave machine while avoiding the reduction to perfect matching. With those future modifications, it’s possible that one really might see a D-Wave speedup that survived serious attempts by skeptics to make it go away. But Helmut was equally clear in saying that, even in such a case, he sees no evidence at present that the speedup would be asymptotic or quantum-computational in nature. In other words, he thinks the existing data is well explained by the observation that we’re comparing D-Wave against classical algorithms for Ising spin minimization problems on Chimera graphs, and D-Wave has heroically engineered an expensive piece of hardware specifically for Ising spin minimization problems on Chimera graphs and basically nothing else. If so, then the prediction would be that such speedups as can be found are unlikely to extend either to more “practical” optimization problems—which need to be embedded into the Chimera graph with considerable losses—or to better scaling behavior on large instances. (As usual, as long as the comparison is against the best classical algorithms, and as long as we grant the classical algorithm the same non-quantum advantages that the D-Wave machine enjoys, such as classical parallelism—as Rønnow et al advocated.) Incidentally, my visit to Texas A&M was partly an “apology tour.” When I announced on this blog that I was moving from MIT to UT Austin, I talked about the challenge and excitement of setting up a quantum computing research center in a place that currently had little quantum computing for hundreds of miles around. This thoughtless remark inexcusably left out not only my friends at Louisiana State (like Jon Dowling and Mark Wilde), but even closer to home, Katzgraber and the others at Texas A&M. I felt terrible about this for months. So it gives me special satisfaction to have the opportunity to call out Katzgraber’s new work in this post. In football, UT and A&M were longtime arch-rivals, but when it comes to the appropriate level of skepticism to apply to quantum supremacy claims, the Texas Republic seems remarkably unified. When 15 MilliKelvin is Toasty In other D-Wave-related scientific news, on Monday night Tameem Albash, Victor Martin-Mayer, and Itay Hen put out a preprint arguing that, in order for quantum annealing to have any real chance of yielding a speedup over classical optimization methods, the temperature of the annealer should decrease at least like 1/log(n), where n is the instance size, and more likely like 1/nβ (i.e., as an inverse power law). If this is correct, then cold as the D-Wave machine is, at 0.015 degrees or whatever above absolute zero, it still wouldn’t be cold enough to see a scalable speedup, at least not without quantum fault-tolerance, something that D-Wave has so far eschewed. With no error-correction, any constant temperature that’s above zero would cause dangerous level-crossings up to excited states when the instances get large enough. Only a temperature that actually converged to zero as the problems got larger would suffice. Over the last few years, I’ve heard many experts make this exact same point in conversation, but this is the first time I’ve seen the argument spelled out in a paper, with explicit calculations (modulo assumptions) of the rate at which the temperature would need to go to zero for uncorrected quantum annealing to be a viable path to a speedup. I lack the expertise to evaluate the calculations myself, but any experts who’d like to share their insight in the comments section are “warmly” (har har) invited. “Their Current Numbers Are Still To Be Checked” As some of you will have seen, The Economist now has a sprawling 10-page cover story about quantum computing and other quantum technologies. I had some contact with the author while the story was in the works. The piece covers a lot of ground and contains many true statements. It could be much worse. But I take issue with two things. First, The Economist claims: “What is notable about the effort [to build scalable QCs] now is that the challenges are no longer scientific but have become matters of engineering.” As John Preskill and others pointed out, this is pretty far from true, at least if we interpret the claim in the way most engineers and businesspeople would. Yes, we know the rules of quantum mechanics, and the theory of quantum fault-tolerance, and a few promising applications; and the basic building blocks of QC have already been demonstrated in several platforms. But if (let’s say) someone were to pony up$100 billion, asking only for a universal quantum computer as soon as possible, I think the rational thing to do would be to spend initially on a frenzy of basic research: should we bet on superconducting qubits, trapped ions, nonabelian anyons, photonics, a combination thereof, or something else?  (Even that is far from settled.)  Can we invent better error-correcting codes and magic state distillation schemes, in order to push the resource requirements for universal QC down by three or four orders of magnitude?  Which decoherence mechanisms will be relevant when we try to do this stuff at scale?  And of course, which new quantum algorithms can we discover, and which new cryptographic codes resistant to quantum attack?

The second statement I take issue with is this:

“For years experts questioned whether the [D-Wave] devices were actually exploiting quantum mechanics and whether they worked better than traditional computers.  Those questions have since been conclusively answered—yes, and sometimes”

1. depends on what you mean by “exploit” (yes, there are quantum tunneling effects, but do they help you solve problems faster?), and
2. no, the evidence remains weak to nonexistent that the D-Wave machine solves anything faster than a traditional computer—certainly if, by “traditional computer,” we mean a device that gets all the advantages of the D-Wave machine (e.g., classical parallelism, hardware heroically specialized to the one type of problem we’re testing on), but no quantum effects.

Shortly afterward, when discussing the race to achieve “quantum supremacy” (i.e., a clear quantum computing speedup for some task, not necessarily a useful one), the Economist piece hedges: “D-Wave has hinted it has already [achieved quantum supremacy], but has made similar claims in the past; their current numbers are still to be checked.”

To me, “their current numbers are still to be checked” deserves its place alongside “mistakes were made” among the great understatements of the English language—perhaps a fitting honor for The Economist.

Defeat Device

Some of you might also have seen that D-Wave announced a deal with Volkswagen, to use D-Wave machines for traffic flow.  I had some advance warning of this deal, when reporters called asking me to comment on it.  At least in the materials I saw, no evidence is discussed that the D-Wave machine actually solves whatever problem VW is interested in faster than it could be solved with a classical computer.  Indeed, in a pattern we’ve seen repeatedly for the past decade, the question of such evidence is never even directly confronted or acknowledged.

So I guess I’ll say the same thing here that I said to the journalists.  Namely, until there’s a paper or some other technical information, obviously there’s not much I can say about this D-Wave/Volkswagen collaboration.  But it would be astonishing if quantum supremacy were to be achieved on an application problem of interest to a carmaker, even as scientists struggle to achieve that milestone on contrived and artificial benchmarks, even as the milestone seems repeatedly to elude D-Wave itself on contrived and artificial benchmarks.  In the previous such partnerships—such as that with Lockheed Martin—we can reasonably guess that no convincing evidence for quantum supremacy was found, because if it had been, it would’ve been trumpeted from the rooftops.

Anyway, I confess that I couldn’t resist adding a tiny snark—something about how, if these claims of amazing performance were found not to withstand an examination of the details, it would not be the first time in Volkswagen’s recent history.

Farewell to a Visionary Leader—One Who Was Trash-Talking Critics on Social Media A Decade Before President Trump

This isn’t really news, but since it happened since my last D-Wave post, I figured I should share.  Apparently D-Wave’s outspoken and inimitable founder, Geordie Rose, left D-Wave to form a machine-learning startup (see D-Wave’s leadership page, where Rose is absent).  I wish Geordie the best with his new venture.

Martinis Visits UT Austin

On Feb. 22, we were privileged to have John Martinis of Google visit UT Austin for a day and give the physics colloquium.  Martinis concentrated on the quest to achieve quantum supremacy, in the near future, using sampling problems inspired by theoretical proposals such as BosonSampling and IQP, but tailored to Google’s architecture.  He elaborated on Google’s plan to build a 49-qubit device within the next few years: basically, a 7×7 square array of superconducting qubits with controllable nearest-neighbor couplings.  To a layperson, 49 qubits might sound unimpressive compared to D-Wave’s 2000—but the point is that these qubits will hopefully maintain coherence times thousands of times longer than the D-Wave qubits, and will also support arbitrary quantum computations (rather than only annealing).  Obviously I don’t know whether Google will succeed in its announced plan, but if it does, I’m very optimistic about a convincing quantum supremacy demonstration being possible with this sort of device.

Perhaps most memorably, Martinis unveiled some spectacular data, which showed near-perfect agreement between Google’s earlier 9-qubit quantum computer and the theoretical predictions for a simulation of the Hofstadter butterfly (incidentally invented by Douglas Hofstadter, of Gödel, Escher, Bach fame, when he was still a physics graduate student).  My colleague Andrew Potter explained to me that the Hofstadter butterfly can’t be used to show quantum supremacy, because it’s mathematically equivalent to a system of non-interacting fermions, and can therefore be simulated in classical polynomial time.  But it’s certainly an impressive calibration test for Google’s device.

2000 Qubits Are Easy, 50 Qubits Are Hard

Just like the Google group, IBM has also publicly set itself the ambitious goal of building a 50-qubit superconducting quantum computer in the near future (i.e., the next few years).  Here in Austin, IBM held a quantum computing session at South by Southwest, so I went—my first exposure of any kind to SXSW.  There were 10 or 15 people in the audience; the purpose of the presentation was to walk through the use of the IBM Quantum Experience in designing 5-qubit quantum circuits and submitting them first to a simulator and then to IBM’s actual superconducting device.  (To the end user, of course, the real machine differs from the simulation only in that with the former, you can see the exact effects of decoherence.)  Afterward, I chatted with the presenters, who were extremely friendly and knowledgeable, and relieved (they said) that I found nothing substantial to criticize in their summary of quantum computing.

Hope everyone had a great Pi Day and Ides of March.

### “THE TALK”: My quantum computing cartoon with Zach Weinersmith

Wednesday, December 14th, 2016

OK, here’s the big entrée that I promised you yesterday:

“THE TALK”: My joint cartoon about quantum comgputing with Zach Weinersmith of SMBC Comics.

In case you’re wondering how this came about: after our mutual friend Sean Carroll introduced me and Zach for a different reason, the idea of a joint quantum computing comic just seemed too good to pass up.  The basic premise—“The Talk”—was all Zach.  I dutifully drafted some dialogue for him, which he then improved and illustrated.  I.e., he did almost all the work (despite having a newborn competing for his attention!).  Still, it was an honor for me to collaborate with one of the great visual artists of our time, and I hope you like the result.  Beyond that, I’ll let the work speak for itself.

### Google, D-Wave, and the case of the factor-10^8 speedup for WHAT?

Wednesday, December 9th, 2015

Update (Dec. 16):  If you’re still following this, please check out an important comment by Alex Selby, the discoverer of Selby’s algorithm, which I discussed in the post.  Selby queries a few points in the Google paper: among other things, he disagrees with their explanation of why his classical algorithm works so well on D-Wave’s Chimera graph (and with their prediction that it should stop working for larger graphs), and he explains that Karmarkar-Karp is not the best known classical algorithm for the Number Partitioning problem.  He also questions whether simulated annealing is the benchmark against which everything should be compared (on the grounds that “everything else requires fine-tuning”), pointing out that SA itself typically requires lots of tuning to get it to work well.

Update (Dec. 11): MIT News now has a Q&A with me about the new Google paper. I’m really happy with how the Q&A turned out; people who had trouble understanding this blog post might find the Q&A easier. Thanks very much to Larry Hardesty for arranging it.

Meanwhile, I feel good that there seems to have been actual progress in the D-Wave debate! In previous rounds, I had disagreed vehemently with some of my MIT colleagues (like Ed Farhi and Peter Shor) about the best way to respond to D-Wave’s announcements. Today, though, at our weekly group meeting, there was almost no daylight between any of us. Partly, I’m sure, it’s that I’ve learned to express myself better; partly it’s that the “trigger” this time was a serious research paper by a group separate from D-Wave, rather than some trash-talking statement from Geordie Rose. But mostly it’s that, thanks to the Google group’s careful investigations, this time pretty much anyone who knows anything agrees about all the basic facts, as I laid them out in this blog post and in the Q&A. All that remains are some small differences in emotional attitude: e.g., how much of your time do you want to spend on a speculative, “dirty” approach to quantum computing (which is far ahead of everyone else in terms of engineering and systems integration, but which still shows no signs of an asymptotic speedup over the best classical algorithms, which is pretty unsurprising given theoretical expectations), at a time when the “clean” approaches might finally be closing in on the long-sought asymptotic quantum speedup?

Another Update: Daniel Lidar was nice enough to email me an important observation, and to give me permission to share it here.  Namely, the D-Wave 2X has a minimum annealing time of 20 microseconds.  Because of this, the observed running times for small instance sizes are artificially forced upward, making the growth rate in the machine’s running time look milder than it really is.  (Regular readers might remember that exactly the same issue plagued previous D-Wave vs. classical performance comparisons.)  Correcting this would certainly decrease the D-Wave 2X’s predicted speedup over simulated annealing, in extrapolations to larger numbers of qubits than have been tested so far (although Daniel doesn’t know by how much).  Daniel stresses that he’s not criticizing the Google paper, which explicitly mentions the minimum annealing time—just calling attention to something that deserves emphasis.

In retrospect, I should’ve been suspicious, when more than a year went by with no major D-Wave announcements that everyone wanted me to react to immediately. Could it really be that this debate was over—or not “over,” but where it always should’ve been, in the hands of experts who might disagree vehemently but are always careful to qualify speedup claims—thereby freeing up the erstwhile Chief D-Wave Skeptic for more “””rewarding””” projects, like charting a middle path through the Internet’s endless social justice wars?

Nope.

As many of you will have seen by now, on Monday a team at Google put out a major paper reporting new experiments on the D-Wave 2X machine.  (See also Hartmut Neven’s blog post about this.)  The predictable popularized version of the results—see for example here and here—is that the D-Wave 2X has now demonstrated a factor-of-100-million speedup over standard classical chips, thereby conclusively putting to rest the question of whether the device is “truly a quantum computer.”  In the comment sections of one my previous posts, D-Wave investor Steve Jurvetson even tried to erect a victory stele, by quoting Karl Popper about falsification.

In situations like this, the first thing I do is turn to Matthias Troyer, who’s arguably the planet’s most balanced, knowledgeable, trustworthy interpreter of quantum annealing experiments. Happily, in collaboration with Ilia Zintchenko and Ethan Brown, Matthias was generous enough to write a clear 3-page document putting the new results into context, and to give me permission to share it on this blog. From a purely scientific standpoint, my post could end right here, with a link to their document.

Then again, from a purely scientific standpoint, the post could’ve ended even earlier, with the link to the Google paper itself!  For this is not a case where the paper hides some crucial issue that the skeptics then need to ferret out.  On the contrary, the paper’s authors include some of the most careful people in the business, and the paper explains the caveats as clearly as one could ask.  In some sense, then, all that’s left for me or Matthias to do is to tell you what you’d learn if you read the paper!

So, OK, has the D-Wave 2X demonstrated a factor-108 speedup or not?  Here’s the shortest answer that I think is non-misleading:

Yes, there’s a factor-108 speedup that looks clearly asymptotic in nature, and there’s also a factor-108 speedup over Quantum Monte Carlo. But the asymptotic speedup is only if you compare against simulated annealing, while the speedup over Quantum Monte Carlo is only constant-factor, not asymptotic. And in any case, both speedups disappear if you compare against other classical algorithms, like that of Alex Selby. Also, the constant-factor speedup probably has less to do with quantum mechanics than with the fact that D-Wave built extremely specialized hardware, which was then compared against a classical chip on the problem of simulating the specialized hardware itself (i.e., on Ising spin minimization instances with the topology of D-Wave’s Chimera graph). Thus, while there’s been genuine, interesting progress, it remains uncertain whether D-Wave’s approach will lead to speedups over the best known classical algorithms, let alone to speedups over the best known classical algorithms that are also asymptotic or also of practical importance. Indeed, all of these points also remain uncertain for quantum annealing as a whole.

To expand a bit, there are really three separate results in the Google paper:

1. The authors create Chimera instances with tall, thin energy barriers blocking the way to the global minimum, by exploiting the 8-qubit “clusters” that play such a central role in the Chimera graph.  In line with a 2002 theoretical prediction by Farhi, Goldstone, and Gutmann (a prediction we’ve often discussed on this blog), they then find that on these special instances, quantum annealing reaches the global minimum exponentially faster than classical simulated annealing, and that the D-Wave machine realizes this advantage.  As far as I’m concerned, this completely nails down the case for computationally-relevant collective quantum tunneling in the D-Wave machine, at least within the 8-qubit clusters.  On the other hand, the authors point out that there are other classical algorithms, like that of Selby (building on Hamze and de Freitas), which group together the 8-bit clusters into 256-valued mega-variables, and thereby get rid of the energy barrier that kills simulated annealing.  These classical algorithms are found empirically to outperform the D-Wave machine.  The authors also match the D-Wave machine’s asymptotic performance (though not the leading constant) using Quantum Monte Carlo, which (despite its name) is a classical algorithm often used to find quantum-mechanical ground states.
2. The authors make a case that the ability to tunnel past tall, thin energy barriers—i.e., the central advantage that quantum annealing has been shown to have over classical annealing—might be relevant to at least some real-world optimization problems.  They do this by studying a classic NP-hard problem called Number Partitioning, where you’re given a list of N positive integers, and your goal is to partition the integers into two subsets whose sums differ from each other by as little as possible.  Through numerical studies on classical computers, they find that quantum annealing (in the ideal case) and Quantum Monte Carlo should both outperform simulated annealing, by roughly equal amounts, on random instances of Number Partitioning.  Note that this part of the paper doesn’t involve any experiments on the D-Wave machine itself, so we don’t know whether calibration errors, encoding loss, etc. will kill the theoretical advantage over simulated annealing.  But even if not, this still wouldn’t yield a “true quantum speedup,” since (again) Quantum Monte Carlo is a perfectly-good classical algorithm, whose asymptotics match those of quantum annealing on these instances.
3. Finally, on the special Chimera instances with the tall, thin energy barriers, the authors find that the D-Wave 2X reaches the global optimum about 108 times faster than Quantum Monte Carlo running on a single-core classical computer.  But, extremely interestingly, they also find that this speedup does not grow with problem size; instead it simply saturates at ~108.  In other words, this is a constant-factor speedup rather than an asymptotic one.  Now, obviously, solving a problem “only” 100 million times faster (rather than asymptotically faster) can still have practical value!  But it’s crucial to remember that this constant-factor speedup is only observed for the Chimera instances—or in essence, for “the problem of simulating the D-Wave machine itself”!  If you wanted to solve something of practical importance, you’d first need to embed it into the Chimera graph, and it remains unclear whether any of the constant-factor speedup would survive that embedding.  In any case, while the paper isn’t explicit about this, I gather that the constant-factor speedup disappears when one compares against (e.g.) the Selby algorithm, rather than against QMC.

So then, what do I say to Steve Jurvetson?  I say—happily, not grudgingly!—that the new Google paper provides the clearest demonstration so far of a D-Wave device’s capabilities.  But then I remind him of all the worries the QC researchers had from the beginning about D-Wave’s whole approach: the absence of error-correction; the restriction to finite-temperature quantum annealing (moreover, using “stoquastic Hamiltonians”), for which we lack clear evidence for a quantum speedup; the rush for more qubits rather than better qubits.  And I say: not only do all these worries remain in force, they’ve been thrown into sharper relief than ever, now that many of the side issues have been dealt with.  The D-Wave 2X is a remarkable piece of engineering.  If it’s still not showing an asymptotic speedup over the best known classical algorithms—as the new Google paper clearly explains that it isn’t—then the reasons are not boring or trivial ones.  Rather, they seem related to fundamental design choices that D-Wave made over a decade ago.

The obvious question now is: can D-Wave improve its design, in order to get a speedup that’s asymptotic, and that holds against all classical algorithms (including QMC and Selby’s algorithm), and that survives the encoding of a “real-world” problem into the Chimera graph?  Well, maybe or maybe not.  The Google paper returns again and again to the subject of planned future improvements to the machine, and how they might clear the path to a “true” quantum speedup. Roughly speaking, if we rule out radical alterations to D-Wave’s approach, there are four main things one would want to try, to see if they helped:

1. Lower temperatures (and thus, longer qubit lifetimes, and smaller spectral gaps that can be safely gotten across without jumping up to an excited state).
2. Better calibration of the qubits and couplings (and thus, ability to encode a problem of interest, like the Number Partitioning problem mentioned earlier, to greater precision).
3. The ability to apply “non-stoquastic” Hamiltonians.  (D-Wave’s existing machines are all limited to stoquastic Hamiltonians, defined as Hamiltonians all of whose off-diagonal entries are real and non-positive.  While stoquastic Hamiltonians are easier from an engineering standpoint, they’re also the easiest kind to simulate classically, using algorithms like QMC—so much so that there’s no consensus on whether it’s even theoretically possible to get a true quantum speedup using stoquastic quantum annealing.  This is a subject of active research.)
4. Better connectivity among the qubits (thereby reducing the huge loss that comes from taking problems of practical interest, and encoding them in the Chimera graph).

(Note that “more qubits” is not on this list: if a “true quantum speedup” is possible at all with D-Wave’s approach, then the 1000+ qubits that they already have seem like more than enough to notice it.)

Anyway, these are all, of course, things D-Wave knows about and will be working on in the near future. As well they should! But to repeat: even if D-Wave makes all four of these improvements, we still have no idea whether they’ll see a true, asymptotic, Selby-resistant, encoding-resistant quantum speedup. We just can’t say for sure that they won’t see one.

In the meantime, while it’s sometimes easy to forget during blog-discussions, the field of experimental quantum computing is a proper superset of D-Wave, and things have gotten tremendously more exciting on many fronts within the last year or two.  In particular, the group of John Martinis at Google (Martinis is one of the coauthors of the Google paper) now has superconducting qubits with orders of magnitude better coherence times than D-Wave’s qubits, and has demonstrated rudimentary quantum error-correction on 9 of them.  They’re now talking about scaling up to ~40 super-high-quality qubits with controllable couplings—not in the remote future, but in, like, the next few years.  If and when they achieve that, I’m extremely optimistic that they’ll be able to show a clear quantum advantage for something (e.g., some BosonSampling-like sampling task), if not necessarily something of practical importance.  IBM Yorktown Heights, which I visited last week, is also working (with IARPA funding) on integrating superconducting qubits with many-microsecond coherence times.  Meanwhile, some of the top ion-trap groups, like Chris Monroe’s at the University of Maryland, are talking similarly big about what they expect to be able to do soon. The “academic approach” to QC—which one could summarize as “understand the qubits, control them, keep them alive, and only then try to scale them up”—is finally bearing some juicy fruit.

(At last week’s IBM conference, there was plenty of D-Wave discussion; how could there not be? But the physicists in attendance—I was almost the only computer scientist there—seemed much more interested in approaches that aim for longer-laster qubits, fault-tolerance, and a clear asymptotic speedup.)

I still have no idea when and if we’ll have a practical, universal, fault-tolerant QC, capable of factoring 10,000-digit numbers and so on.  But it’s now looking like only a matter of years until Gil Kalai, and the other quantum computing skeptics, will be forced to admit they were wrong—which was always the main application I cared about anyway!

So yeah, it’s a heady time for QC, with many things coming together faster than I’d expected (then again, it was always my personal rule to err on the side of caution, and thereby avoid contributing to runaway spirals of hype).  As we stagger ahead into this new world of computing—bravely, coherently, hopefully non-stoquastically, possibly fault-tolerantly—my goal on this blog will remain what it’s been for a decade: not to prognosticate, not to pick winners, but merely to try to understand and explain what has and hasn’t already been shown.

Update (Dec. 10): Some readers might be interested in an economic analysis of the D-Wave speedup by commenter Carl Shulman.

Another Update: Since apparently some people didn’t understand this post, here are some comments from a Y-Combinator thread about the post that might be helpful:

(1) [T]he conclusion of the Google paper is that we have probable evidence that with enough qubits and a big enough problem it will be faster for a very specific problem compared to a non-optimal classical algorithm (we have ones that are for sure better).

This probably sounds like a somewhat useless result (quantum computer beats B-team classical algorithm), but it is in fact interesting because D-Wave’s computers are designed to perform quantum annealing and they are comparing it to simulated annealing (the somewhat analogous classical algorithm). However they only found evidence of a constant (i.e. one that 4000 qubits wouldn’t help with) speed up (though a large one) compared to a somewhat better algorithm (Quantum Monte Carlo, which is ironically not a quantum algorithm), and they still can’t beat an even better classical algorithm (Selby’s) at all, even in a way that won’t scale.

Scott’s central thesis is that although it is possible there could be a turning point past 2000 qubits where the D-Wave will beat our best classical alternative, none of the data collected so far suggests that. So it’s possible that a 4000 qubit D-Wave machine will exhibit this trend, but there is no evidence of it (yet) from examining a 2000 qubit machine. Scott’s central gripe with D-Wave’s approach is that they don’t have any even pie-in-the-sky theoretical reason to expect this to happen, and scaling up quantum computers without breaking the entire process is much harder than for classical computers so making them even bigger doesn’t seem like a solution.

(2) DWave machines are NOT gate quantum computers; they call their machine quantum annealing machines. It is not known the complexity class of problems that can be solved efficiently by quantum annealing machines, or if that class is equivalent to classical machines.

The result shows that the DWave machine is asymptotically faster than the Simulated Annealing algorithm (yay!), which suggests that it is executing the Quantum Annealing algorithm. However, the paper also explicitly states that this does not mean that the Dwave machine is exhibiting a ‘quantum speedup’. To do this, they would need to show it to outperform the best known classical algorithm, which as the paper acknowledges, it does not.

What the paper does seem to be showing is that the machine in question is actually fundamentally quantum in nature; it’s just not clear yet that that the type of quantum computer it is is an improvement over classical ones.

(3) [I]t isn’t called out in the linked blog since by now Scott probably considers it basic background information, but D-Wave only solves a very particular problem, and it is both not entirely clear that it has a superior solution to that problem than a classical algorithm can obtain and it is not clear that encoding real problems into that problem will not end up costing you all of the gains itself. Really pragmatic applications are still a ways into the future. It’s hard to imagine what they might be when we’re still so early in the process, and still have no good idea what either the practical or theoretical limits are.

(4) The popular perception of quantum computers as “doing things in parallel” is very misleading. A quantum computer lets you perform computation on a superposed state while maintaining that superposition. But that only helps if the structure of the problem lets you somehow “cancel out” the incorrect results leaving you with the single correct one. [There’s hope for the world! –SA]

Wednesday, August 26th, 2015

A bunch of people have asked me to comment on D-Wave’s release of its 1000-qubit processor, and a paper by a group including Cathy McGeoch saying that the machine is 1 or 2 orders of faster (in annealing time, not wall-clock time) than simulated annealing running on a single-core classical computer.  It’s even been suggested that the “Scott-signal” has been shining brightly for a week above Quantham City, but that Scott-man has been too lazy and out-of-shape even to change into his tights.

Scientifically, it’s not clear if much has changed.  D-Wave now has a chip with twice as many qubits as the last one.  That chip continues to be pretty effective at finding its own low-energy states: indeed, depending on various details of definition, the machine can even find its own low-energy states “faster” than some implementation of simulated annealing running on a single-core chip.  Of course, it’s entirely possible that Matthias Troyer or Sergei Isakov or Troels Ronnow or someone like that will be able to find a better implementation of simulated annealing that closes even the modest gap—as happened the last time—but I’ll give the authors the benefit of the doubt that they put good-faith effort into optimizing the classical code.

More importantly, I’d say it remains unclear whether any of the machine’s performance on the instances tested here can be attributed to quantum tunneling effects.  In fact, the paper explicitly states (see page 3) that it’s not going to consider such questions, and I think the authors would agree that you could very well see results like theirs, even if what was going on was fundamentally classical annealing.  Also, of course, it’s still true that, if you wanted to solve a practical optimization problem, you’d first need to encode it into the Chimera graph, and that reduction entails a loss that could hand a decisive advantage to simulated annealing, even without the need to go to multiple cores.  (This is what I’ve described elsewhere as essentially all of these performance comparisons taking place on “the D-Wave machine’s home turf”: that is, on binary constraint satisfaction problems that have precisely the topology of D-Wave’s Chimera graph.)

But, I dunno, I’m just not feeling the urge to analyze this in more detail.  Part of the reason is that I think the press might be getting less hyper-excitable these days, thereby reducing the need for a Chief D-Wave Skeptic.  By this point, there may have been enough D-Wave announcements that papers realize they no longer need to cover each one like an extraterrestrial landing.  And there are more hats in the ring now, with John Martinis at Google seeking to build superconducting quantum annealing machines but with ~10,000x longer coherence times than D-Wave’s, and with IBM Research and some others also trying to scale superconducting QC.  The realization has set in, I think, that both D-Wave and the others are in this for the long haul, with D-Wave currently having lots of qubits, but with very short coherence times and unclear prospects for any quantum speedup, and Martinis and some others having qubits of far higher quality, but not yet able to couple enough of them.

The other issue is that, on my flight from Seoul back to Newark, I watched two recent kids’ movies that were almost defiant in their simple, unironic, 1950s-style messages of hope and optimism.  One was Disney’s new live-action Cinderella; the other was Brad Bird’s Tomorrowland.  And seeing these back-to-back filled me with such positivity and good will that, at least for these few hours, it’s hard to summon my usual crusty self.  I say, let’s invent the future together, and build flying cars and jetpacks in our garages!  Let a thousand creative ideas bloom for how to tackle climate change and the other crises facing civilization!  (Admittedly, mass-market flying cars and jetpacks are probably not a step forward on climate change … but, see, there’s that negativity coming back.)  And let another thousand ideas bloom for how to build scalable quantum computers—sure, including D-Wave’s!  Have courage and be kind!

So yeah, if readers would like to discuss the recent D-Wave paper further (especially those who know something about it), they’re more than welcome to do so in the comments section.  But I’ve been away from Dana and Lily for two weeks, and will endeavor to spend time with them rather than obsessively reloading the comments (let’s see if I succeed).

As a small token of my goodwill, I enclose two photos from my last visit to a D-Wave machine, which occurred when I met with some grad students in Waterloo this past spring.  As you can see, I even personally certified that the machine was operating as expected.  But more than that: surpassing all reasonable expectations for quantum AI, this model could actually converse intelligently, through a protruding head resembling that of IQC grad student Sarah Kaiser.

### Memrefuting

Wednesday, February 11th, 2015

(in which I bring this blog back to the “safe, uncontroversial” territory of arguing with people who think they can solve NP-complete problems in polynomial time)

A few people have asked my opinion about “memcomputing”: a computing paradigm that’s being advertised, by its developers, as a way to solve NP-complete problems in polynomial time.  According to the paper Memcomputing NP-complete problems in polynomial time using polynomial resources and collective states, memcomputing “is based on the brain-like notion that one can process and store information within the same units (memprocessors) by means of their mutual interactions.”  The authors are explicit that, in their view, this idea allows the Subset Sum problem to be solved with polynomial resources, by exploring all 2n possible subsets in parallel, and that this refutes the Extended Church-Turing Thesis.  They’ve actually built ‘memcomputers’ that solve small instances of Subset Sum, and they hope to scale them up, though they mention hardware limitations that have made doing so difficult—more about that later.

A bunch of people (on Hacker News, Reddit, and elsewhere) tried to explain the problems with the Subset Sum claim when the above preprint was posted to the arXiv last year.  However, an overlapping set of authors has now simply repeated the claim, unmodified, in a feature article in this month’s Scientific American.  Unfortunately the SciAm article is behind a paywall, but here’s the relevant passage:

Memcomputing really shows advantages when applied to one of the most difficult types of problems we know of in computer science: calculating all the properties of a large series of integers. This is the kind of challenge a computer faces when trying to decipher complex codes. For instance, give the computer 100 integers and then ask it to find at least one subset that adds up to zero. The computer would have to check all possible subsets and then sum all numbers in each subset. It would plow through each possible combination, one by one, which is an exponentially huge increase in processing time. If checking 10 integers took one second, 100 integers would take 1027 seconds—millions of trillions of years … [in contrast,] a memcomputer can calculate all subsets and sums in just one step, in true parallel fashion, because it does not have to shuttle them back and forth to a processor (or several processors) in a series of sequential steps. The single-step approach would take just a single second.

For those tuning in from home: in the Subset Sum problem, we’re given n integers a1,…,an, and we want to know whether there exists a subset of them that sums to a target integer k.  (To avoid trivializing the problem, either k should be nonzero or else the subset should be required to be nonempty, a mistake in the passage quoted above.)

To solve Subset Sum in polynomial time, the basic idea of “memcomputing” is to generate waves at frequencies that encode the sums of all possible subsets of ai‘s, and then measure the resulting signal to see if there’s a frequency there that corresponds to k.

Alas, there’s a clear scalability problem that seems to me to completely kill this proposal, as a practical way of solving NP-complete problems.  The problem is that the signal being measured is (in principle!) a sum of waves of exponentially many different frequencies.  By measuring this wave and taking a Fourier transform, one will not be able to make out the individual frequencies until one has monitored the signal for an exponential amount of time.  There are actually two issues here:

(1) Even if there were just a single frequency, measuring the frequency to exponential precision will take exponential time. This can be easily seen by contemplating even a moderately large n.  Thus, suppose n=1000.  Then we would need to measure a frequency to a precision of one part in ~21000. If the lowest frequency were (say) 1Hz, then we would be trying to distinguish frequencies that differ by far less than the Planck scale.  But distinguishing frequencies that close would require so much energy that one would exceed the Schwarzschild limit and create a black hole!  The alternative is to make the lowest frequency slower than the lifetime of the universe, causing an exponential blowup in the amount of time we need to run the experiment.

(2) Because there are exponentially many frequencies, the amplitude of each frequency will get attenuated by an exponential amount.  Again, suppose that n=1000, so that we’re talking about attenuation by a ~2-1000 factor.  Then given any amount of input radiation that could be gathered in physical universe, the expected amount of amplitude on each frequency would correspond to a microscopically small fraction of 1 photon — so again, it would take exponential time for us to notice any radiation at all on the frequency that interests us (unless we used an insensitive test that was liable to confuse that frequency with many other nearby frequencies).

What do the authors have to say about these issues?  Here are the key passages from the above-mentioned paper:

all frequencies involved in the collective state (1) are dampened by the factor 2-n.  In the case of the ideal machine, i.e., a noiseless machine, this would not represent an issue because no information is lost.  On the contrary, when noise is accounted for, the exponential factor represents the hardest limitation of the experimentally fabricated machine, which we reiterate is a technological limit for this particular realization of a memcomputing machine but not for all of them …

In conclusion we have demonstrated experimentally a deterministic memcomputing machine that is able to solve an NP-complete problem in polynomial time (actually in one step) using only polynomial resources.  The actual machine we built clearly suffers from technological limitations due to unavoidable noise that impair [sic] the scalability.  This issue can, however, be overcome in other UMMs [universal memcomputing machines] using other ways to encode such information.

The trouble is that no other way to encode such information is ever mentioned.  And that’s not an accident: as explained above, when n becomes even moderately large, this is no longer a hardware issue; it’s a fundamental physics issue.

It’s important to realize that the idea of solving NP-complete problems in polynomial time using an analog device is far from new: computer scientists discussed such ideas extensively in the 1960s and 1970s.  Indeed, the whole point of my NP-complete Problems and Physical Reality paper was to survey the history of such attempts, and (hopefully!) to serve as a prophylactic against people making more such attempts without understanding the history.  For computer scientists ultimately came to realize that all proposals along these lines simply “smuggle the exponentiality” somewhere that isn’t being explicitly considered, exactly like all proposals for perpetual-motion machines smuggle the entropy increase somewhere that isn’t being explicitly considered.  The problem isn’t a practical one; it’s one of principle.  And I find it unfortunate that the recent memcomputing papers show no awareness of this story.

(Incidentally, quantum computing is interesting precisely because, out of all “post-Extended-Church-Turing” computing proposals, it’s the only one for which we can’t articulate a clear physical reason why it won’t scale, analogous to the reasons given above for memcomputing.  With quantum computing the tables are turned, with the skeptics forced to handwave about present-day practicalities, while the proponents wield the sharp steel of accepted physical law.  But as readers of this blog well know, quantum computing doesn’t seem to promise the polynomial-time solution of NP-complete problems, only of more specialized problems.)

### Quantum Machine Learning Algorithms: Read the Fine Print

Monday, February 2nd, 2015

So, I’ve written a 4-page essay of that title, which examines the recent spate of quantum algorithms for clustering, classification, support vector machines, and other “Big Data” problems that grew out of a 2008 breakthrough on solving linear systems by Harrow, Hassidim, and Lloyd, as well as the challenges in applying these algorithms to get genuine exponential speedups over the best classical algorithms.  An edited version of the essay will be published as a Commentary in Nature Physics.  Thanks so much to Iulia Georgescu at Nature for suggesting that I write this.

Update (April 4, 2015): The piece has now been published.

### Der Quantencomputer

Friday, November 14th, 2014

### Speaking Truth to Parallelism at Cornell

Friday, October 3rd, 2014

This week I was at my alma mater, Cornell, to give a talk at the 50th anniversary celebration of its computer science department.  You can watch the streaming video here; my talk runs from roughly 1:17:30 to 1:56 (though if you’ve seen other complexity/physics/humor shows by me, this one is pretty similar, except for the riff about Cornell at the beginning).

Along with the 50th anniversary celebration, Bill Gates was also on campus to dedicate Bill and Melinda Gates Hall, the new home of Cornell’s CS department.  Click here for streaming video of a Q&A that Gates did with Cornell students, where I thought he acquitted himself quite well, saying many sensible things about education, the developing world, etc. that other smart people could also say, but that have extra gravitas coming from him.  Gates has also become extremely effective at wrapping barbs of fact inside a soft mesh of politically-unthreatening platitudes—but listen carefully and you’ll hear the barbs.  The amount of pomp and preparation around Gates’s visit reminded me of when President Obama visited MIT, befitting the two men’s approximately equal power.  (Obama has nuclear weapons, but then again, he also has Congress.)

And no, I didn’t get to meet Gates or shake his hand, though I did get to stand about ten feet from him at the Gates Hall dedication.  (He apparently spent most of his time at Cornell meeting with plant breeders, and other people doing things relevant to the Gates Foundation’s interests.)

Thanks so much to Bobby and Jon Kleinberg, and everyone else who invited me to this fantastic event and helped make it happen.  May Cornell’s CS department have a great next 50 years.

One last remark before I close this post.  Several readers have expressed disapproval and befuddlement over the proposed title of my next book, “Speaking Truth to Parallelism.”  In the words of commenter TonyK:

That has got to be the worst title in the history of publishing! “Speaking Truth to Parallelism”? It doesn’t even make sense! I count myself as one of your fans, Scott, but you’re going to have to do better than that if you want anybody else to buy your book. I know you can do better — witness “Quantum Computing Since Democritus”.

However, my experiences at Cornell this week helped to convince me that, not only does “Speaking Truth to Parallelism” make perfect sense, it’s an activity that’s needed now more than ever.  What it means, of course, is fighting a certain naïve, long-ago-debunked view of quantum computers—namely, that they would achieve exponential speedups by simply “trying every possible answer in parallel”—that’s become so entrenched in the minds of many journalists, laypeople, and even scientists from other fields that it feels like nothing you say can possibly dislodge it.  The words out of your mouth will literally be ignored, misheard, or even contorted to the opposite of what they mean, if that’s what it takes to preserve the listener’s misconception about quantum computers being able to solve NP-hard optimization problems by sheer magic.  (Much like in the Simpsons-visit-Australia episode, where Marge’s request for “coffee” is misheard over and over as “beer.”)  You probably think I’m exaggerating, and I’d agree with you—if I hadn’t experienced this phenomenon hundreds of times over the last decade.

So, to take one example: after my talk at Cornell, an audience member came up to me to say that it was a wonderful talk, but that what he really wanted to know was whether I thought quantum computers could solve problems in the “NP space” in linear time, by trying all the possible solutions at once.  He didn’t seem to realize that I’d spent the entire previous half hour answering that exact question, explaining why the answer was “no.”  Coincidentally, this week I also got an email from a longtime reader of this blog, saying that he read and loved Quantum Computing Since Democritus, and wanted my feedback on a popular article he’d written about quantum computing.  What was the gist of the article?  You guessed it: “quantum computing = generic exponential speedups for optimization, machine learning, and Big Data problems, by trying all the possible answers at once.”

These people’s enthusiasm for quantum computing tends to be so genuine, so sincere, that I find myself unable to blame them—even when they’ve done the equivalent of going up to Richard Dawkins and thanking him for having taught them that evolution works for the good of the entire species, just as its wise Designer intended.  I do blame the media and other careless or unscrupulous parties for misleading people about quantum computing, but most of all I blame myself, for not making my explanations clear enough.  In the end, then, meeting the “NP space” folks only makes me want to redouble my efforts to Speak Truth to Parallelism: eventually, I feel, the nerd world will get this point.

Update (Oct. 4): I had regarded this (perhaps wrongly) as too obvious to state, but particularly for non-native English speakers, I’d better clarify: “speaking truth to parallelism” is a deliberate pun on the left-wing protester phrase “speaking truth to power.”  So whatever linguistic oddness there is in my phrase, I’d say it simply inherits from the original.

Another Update (Oct. 7): See this comment for my short summary of what’s known about the actual technical question (can quantum computers solve NP-complete problems in polynomial time, or not?).

Another Update (Oct. 8): Many commenters wrote to point out that the video of my talk at Cornell is now password-protected, and no longer publicly available.  I wrote to my contacts at Cornell to ask about this, and they said they’re planning to release lightly-edited versions of the videos soon, but will look into the matter in the meantime.