Those of you who read German (I don’t) might enjoy a joint interview of me and Seth Lloyd about quantum computing, which was conducted in Seth’s office by the journalist Christian Meier, and published in the Swiss newspaper Neue Zürcher Zeitung. Even if you don’t read German, you can just feed the interview into Google Translate, like I did. While the interview covers ground that will be forehead-bangingly familiar to regular readers of this blog, I’m happy with how it turned out; even the slightly-garbled Google Translate output is much better than most quantum computing articles in the English-language press. (And while Christian hoped to provoke spirited debate between me and Seth by interviewing us together, we surprised ourselves by finding very little that we actually disagreed about.) I noticed only one error, when I’m quoted talking about “the discovery of the transistor in the 1960s.” I might have said something about the widespread commercialization of transistors (and integrated circuits) in the 1960s, but I know full well that the transistor was invented at Bell Labs in 1947.
Archive for the ‘Speaking Truth to Parallelism’ Category
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).
The other two things in that video—a talk by Tom Henzinger about IST Austria, a bold new basic research institute that he leads, closely modeled after the Weizmann Institute in Israel; and a discussion panel about the future of programming languages—are also really interesting and worth watching. There was lots of other good stuff at this workshop, including a talk about Google Glass and its applications to photography (by, not surprisingly, a guy wearing a Google Glass—Marc Levoy); a panel discussion with three Turing Award winners, Juris Hartmanis, John Hopcroft, and Ed Clarke, about the early days of Cornell’s CS department; a talk by Amit Singhal, Google’s director of search; a talk about differential privacy by Cynthia Dwork, one of the leading researchers at the recently-closed Microsoft SVC lab (with a poignant and emotional ending); and a talk by my own lab director at MIT, Daniela Rus, about her research in robotics.
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.
A few months ago, I signed a contract with MIT Press to publish a new book: an edited anthology of selected posts from this blog, along with all-new updates and commentary. The book’s tentative title (open to better suggestions) is Speaking Truth to Parallelism: Dispatches from the Frontier of Quantum Computing Theory. The new book should be more broadly accessible than Quantum Computing Since Democritus, although still far from your typical pop-science book. My goal is to have STTP out by next fall, to coincide with Shtetl-Optimized‘s tenth anniversary.
If you’ve been a regular reader, then this book is my way of thanking you for … oops, that doesn’t sound right. If it were a gift, I should give it away for free, shouldn’t I? So let me rephrase: buying this reasonably-priced book can be your way of thanking me, if you’ve enjoyed my blog all these years. But it will also (I hope) be a value-added proposition: not only will you be able to put the book on your coffee table to impress an extremely nerdy subset of your friends, you’ll also get “exclusive content” unavailable on the blog.
To be clear, the posts that make it into the book will be ruthlessly selected: nothing that’s pure procrastination, politics, current events, venting, or travelogue, only the choice fillets that could plausibly be claimed to advance the public understanding of science. Even for those, I’ll add additional background material, and take out digs unworthy of a book (making exceptions for anything that really cracks me up on a second reading).
If I had to pick a unifying theme for the book, I’d sigh and then say: it’s about a certain attitude toward the so-called “deepest questions,” like the nature of quantum mechanics or the ultimate limits of computation or the mind/body problem or the objectivity of mathematics or whether our universe is a computer simulation. It’s an attitude that I wish more popular articles managed to get across, and at any rate, that people ought to adopt when reading those articles. The attitude combines an openness to extraordinary claims, with an unceasing demand for clarity about the nature of those claims, and an impatience whenever that demand is met with evasion, obfuscation, or a “let’s not get into technicalities right now.” It’s an attitude that constantly asks questions like:
“OK, so what can you actually do that’s different?”
“Why doesn’t that produce an absurd result when applied to simple cases?”
“Why isn’t that just a fancy way of saying what I could’ve said in simpler language?”
“Why couldn’t you have achieved the same thing without your ‘magic ingredient’?”
“So what’s your alternative account for how that happens?”
“Why isn’t that obvious?”
“What’s really at stake here?”
“What’s the catch?”
It’s an attitude that accepts the possibility that such questions might have satisfying answers—in which case, a change in worldview will be in order. But not before answers are offered, openly debated, and understood by the community of interested people.
Of all the phrases I use on this blog, I felt “Speaking Truth to Parallelism” best captured the attitude in question. I coined the phrase back in 2007, when D-Wave’s claims to be solving Sudoku puzzles with a quantum computer unleashed a tsunami of journalism about QCs—what they are, how they would work, what they could do—that (in my opinion) perfectly illustrated how not to approach a metaphysically-confusing new technology. Having said that, the endless debate around D-Wave won’t by any means be the focus of this book: it will surface, of course, but only when it helps to illustrate some broader point.
In planning this book, the trickiest issue was what to do with comments. Ultimately, I decided that the comments make Shtetl-Optimized what it is—so for each post I include, I’ll include a brief selection of the most interesting comments, together with my responses to them. My policy will be this: by default, I’ll consider any comments on this blog to be fair game for quoting in the book, in whole or in part, and attributed to whatever handle the commenter used. However, if you’d like to “opt out” of having your comments quoted, I now offer you a three-month window in which to do so: just email me, or leave a comment (!) on this thread. You can also request that certain specific comments of yours not be quoted, or that your handle be removed from your comments, or your full name added to them—whatever you want.
Update (9/24): After hearing from several of you, I’ve decided on the following modified policy. In all cases where I have an email address, I will contact the commenters about any of their comments that I’m thinking of using, to request explicit permission to use them. In the hopefully-rare cases where I can’t reach a given commenter, but where their comment raised what seems like a crucial point requiring a response in the book, I might quote from the comment anyway—but in those cases, I’ll be careful not to reproduce very long passages, in a way that might run afoul of the fair use exception.
OK, so for those who haven’t yet heard: this week Google’s Quantum AI Lab announced that it’s teaming up with John Martinis, of the University of California, Santa Barbara, to accelerate the Martinis group‘s already-amazing efforts in superconducting quantum computing. (See here for the MIT Tech‘s article, here for Wired‘s, and here for the WSJ‘s.) Besides building some of the best (if not the best) superconducting qubits in the world, Martinis, along with Matthias Troyer, was also one of the coauthors of two important papers that found no evidence for any speedup in the D-Wave machines. (However, in addition to working with the Martinis group, Google says it will also continue its partnership with D-Wave, in an apparent effort to keep reality more soap-operatically interesting than any hypothetical scenario one could make up on a blog.)
I have the great honor of knowing John Martinis, even once sharing the stage with him at a “Physics Cafe” in Aspen. Like everyone else in our field, I profoundly admire the accomplishments of his group: they’ve achieved coherence times in the tens of microseconds, demonstrated some of the building blocks of quantum error-correction, and gotten tantalizingly close to the fault-tolerance threshold for the surface code. (When, in D-Wave threads, people have challenged me: “OK Scott, so then which experimental quantum computing groups should be supported more?,” my answer has always been some variant of: “groups like John Martinis’s.”)
So I’m excited about this partnership, and I wish it the very best.
But I know people will ask: apart from the support and well-wishes, do I have any predictions? Alright, here’s one. I predict that, regardless of what happens, commenters here will somehow make it out that I was wrong. So for example, if the Martinis group, supported by Google, ultimately succeeds in building a useful, scalable quantum computer—by emphasizing error-correction, long coherence times (measured in the conventional way), “gate-model” quantum algorithms, universality, and all the other things that D-Wave founder Geordie Rose has pooh-poohed from the beginning—commenters will claim that still most of the credit belongs to D-Wave, for whetting Google’s appetite, and for getting it involved in superconducting QC in the first place. (The unstated implication being that, even if there were little or no evidence that D-Wave’s approach would ever lead to a genuine speedup, we skeptics still would’ve been wrong to state that truth in public.) Conversely, if this venture doesn’t live up to the initial hopes, commenters will claim that that just proves Google’s mistake: rather than “selling out to appease the ivory-tower skeptics,” they should’ve doubled down on D-Wave. Even if something completely different happens—let’s say, Google, UCSB, and D-Wave jointly abandon their quantum computing ambitions, and instead partner with ISIS to establish the world’s first “Qualiphate,” ruling with a niobium fist over California and parts of Oregon—I would’ve been wrong for having failed to foresee that. (Even if I did sort of foresee it in the last sentence…)
Yet, while I’ll never live to see the blog-commentariat acknowledge the fundamental reasonableness of my views, I might live to see scalable quantum computers become a reality, and that would surely be some consolation. For that reason, even if for no others, I once again wish the Martinis group and Google’s Quantum AI Lab the best in their new partnership.
Unrelated Announcement: Check out a lovely (very basic) introductory video on quantum computing and information, narrated by John Preskill and Spiros Michalakis, and illustrated by Jorge Cham of PhD Comics.
So, the Templeton Foundation invited me to write a 1500-word essay on the above question. It’s like a blog post, except they pay me to do it! My essay is now live, here. I hope you enjoy my attempt at techno-futurist prose. You can comment on the essay either here or over at Templeton’s site. Thanks very much to Ansley Roan for commissioning the piece.
Update (April 5): By now, three or four people have written in asking for my reaction to the preprint “Computational solution to quantum foundational problems” by Arkady Bolotin. (See here for the inevitable Slashdot discussion, entitled “P vs. NP Problem Linked to the Quantum Nature of the Universe.”) It gives me no pleasure to respond to this sort of thing—it would be far better to let papers this gobsmackingly uninformed about the relevant issues fade away in quiet obscurity—but since that no longer seems to be possible in the age of social media, my brief response is here.
(note: sorry, no April Fools post, just a post that happens to have gone up on April Fools)
This weekend, Dana and I celebrated our third anniversary by going out to your typical sappy romantic movie: Particle Fever, a documentary about the Large Hadron Collider. As it turns out, the movie was spectacularly good; anyone who reads this blog should go see it. Or, to offer even higher praise:
If watching Particle Fever doesn’t cause you to feel in your bones the value of fundamental science—the thrill of discovery, unmotivated by any application—then you are not truly human. You are a barnyard animal who happens to walk on its hind legs.
Indeed, I regard Particle Fever as one of the finest advertisements for science itself ever created. It’s effective precisely because it doesn’t try to tell you why science is important (except for one scene, where an economist asks a physicist after a public talk about the “return on investment” of the LHC, and is given the standard correct answer, about “what was the return on investment of radio waves when they were first discovered?”). Instead, the movie simply shows you the lives of particle physicists, of people who take for granted the urgency of knowing the truth about the basic constituents of reality. And in showing you the scientists’ quest, it makes you feel as they feel. Incidentally, the movie also shows footage of Congressmen ridiculing the uselessness of the Superconducting Supercollider, during the debates that led to the SSC’s cancellation. So, gently, implicitly, you’re invited to choose: whose side are you on?
I do have a few, not quite criticisms of the movie, but points that any viewer should bear in mind while watching it.
First, it’s important not to come away with the impression that Particle Fever shows “what science is usually like.” Sure, there are plenty of scenes that any scientist would find familiar: sleep-deprived postdocs; boisterous theorists correcting each other’s statements over Chinese food; a harried lab manager walking to the office oblivious to traffic. On the other hand, the decades-long quest to find the Higgs boson, the agonizing drought of new data before the one big money shot, the need for an entire field to coalesce around a single machine, the whole careers hitched to specific speculative scenarios that this one machine could favor or disfavor—all of that is a profoundly abnormal situation in the history of science. Particle physics didn’t used to be that way, and other parts of science are not that way today. Of course, the fact that particle physics became that way makes it unusually suited for a suspenseful movie—a fact that the creators of Particle Fever understood perfectly and exploited to the hilt.
Second, the movie frames the importance of the Higgs search as follows: if the Higgs boson turned out to be relatively light, like 115 GeV, then that would favor supersymmetry, and hence an “elegant, orderly universe.” If, on the other hand, the Higgs turned out to be relatively heavy, like 140 GeV, then that would favor anthropic multiverse scenarios (and hence a “messy, random universe”). So the fact that the Higgs ended up being 125 GeV means the universe is coyly refusing to tell us whether it’s orderly or random, and more research is needed.
In my view, it’s entirely appropriate for a movie like this one to relate its subject matter to big, metaphysical questions, to the kinds of questions anyone can get curious about (in contrast to, say, “what is the mechanism of electroweak symmetry breaking?”) and that the scientists themselves talk about anyway. But caution is needed here. My lay understanding, which might be wrong, is as follows: while it’s true that a lighter Higgs would tend to favor supersymmetric models, the only way to argue that a heavier Higgs would “favor the multiverse,” is if you believe that a multiverse is automatically favored by a lack of better explanations. More broadly, I wish the film had made clearer that the explanation for (some) apparent “fine-tunings” in the Standard Model might be neither supersymmetry, nor the multiverse, nor “it’s just an inexplicable accident,” but simply some other explanation that no one has thought of yet, but that would emerge from a better understanding of quantum field theory. As one example, on reading up on the subject after watching the film, I was surprised to learn that a very conservative-sounding idea—that of “asymptotically safe gravity”—was used in 2009 to predict the Higgs mass right on the nose, at 126.3 ± 2.2 GeV. Of course, it’s possible that this was just a lucky guess (there were, after all, lots of Higgs mass predictions). But as an outsider, I’d love to understand why possibilities like this don’t seem to get discussed more (there might, of course, be perfectly good reasons that I don’t know).
Third, for understandable dramatic reasons, the movie focuses almost entirely on the “younger generation,” from postdocs working on ATLAS and CMS detectors, to theorists like Nima Arkani-Hamed who are excited about the LHC because of its ability to test scenarios like supersymmetry. From the movie’s perspective, the creation of the Standard Model itself, in the 60s and 70s, might as well be ancient history. Indeed, when Peter Higgs finally appears near the end of the film, it’s as if Isaac Newton has walked onstage. At several points, I found myself wishing that some of the original architects of the Standard Model, like Steven Weinberg or Sheldon Glashow, had been interviewed to provide their perspectives. After all, their model is really the one that’s been vindicated at the LHC, not (so far) any of the newer ideas like supersymmetry or large extra dimensions.
OK, but let me come to the main point of this post. I confess that my overwhelming emotion on watching Particle Fever was one of regret—regret that my own field, quantum computing, has never managed to make the case for itself the way particle physics and cosmology have, in terms of the human urge to explore the unknown.
See, from my perspective, there’s a lot to envy about the high-energy physicists. Most importantly, they don’t perceive any need to justify what they do in terms of practical applications. Sure, they happily point to “spinoffs,” like the fact that the Web was invented at CERN. But any time they try to justify what they do, the unstated message is that if you don’t see the inherent value of understanding the universe, then the problem lies with you.
Now, no marketing consultant would ever in a trillion years endorse such an out-of-touch, elitist sales pitch. But the remarkable fact is that the message has more-or-less worked. While the cancellation of the SSC was a setback, the high-energy physicists did succeed in persuading the world to pony up the $11 billion needed to build the LHC, and to gain the information that the mass of the Higgs boson is about 125 GeV.
Now contrast that with quantum computing. To hear the media tell it, a quantum computer would be a powerful new gizmo, sort of like existing computers except faster. (Why would it be faster? Something to do with trying both 0 and 1 at the same time.) The reasons to build quantum computers are things that could make any buzzword-spouting dullard nod in recognition: cracking uncrackable encryption, finding bugs in aviation software, sifting through massive data sets, maybe even curing cancer, predicting the weather, or finding aliens. And all of this could be yours in a few short years—or some say it’s even commercially available today. So, if you check back in a few years and it’s still not on store shelves, probably it went the way of flying cars or moving sidewalks: another technological marvel that just failed to materialize for some reason.
Foolishly, shortsightedly, many academics in quantum computing have played along with this stunted vision of their field—because saying this sort of thing is the easiest way to get funding, because everyone else says the same stuff, and because after you’ve repeated something on enough grant applications you start to believe it yourself. All in all, then, it’s just easier to go along with the “gizmo vision” of quantum computing than to ask pointed questions like:
What happens when it turns out that some of the most-hyped applications of quantum computers (e.g., optimization, machine learning, and Big Data) were based on wildly inflated hopes—that there simply isn’t much quantum speedup to be had for typical problems of that kind, that yes, quantum algorithms exist, but they aren’t much faster than the best classical randomized algorithms? What happens when it turns out that the real applications of quantum computing—like breaking RSA and simulating quantum systems—are nice, but not important enough by themselves to justify the cost? (E.g., when the imminent risk of a quantum computer simply causes people to switch from RSA to other cryptographic codes? Or when the large polynomial overheads of quantum simulation algorithms limit their usefulness?) Finally, what happens when it turns out that the promises of useful quantum computers in 5-10 years were wildly unrealistic?
I’ll tell you: when this happens, the spigots of funding that once flowed freely will dry up, and the techno-journalists and pointy-haired bosses who once sang our praises will turn to the next craze. And they’re unlikely to be impressed when we protest, “no, look, the reasons we told you before for why you should support quantum computing were never the real reasons! and the real reasons remain as valid as ever!”
In my view, we as a community have failed to make the honest case for quantum computing—the case based on basic science—because we’ve underestimated the public. We’ve falsely believed that people would never support us if we told them the truth: that while the potential applications are wonderful cherries on the sundae, they’re not and have never been the main reason to build a quantum computer. The main reason is that we want to make absolutely manifest what quantum mechanics says about the nature of reality. We want to lift the enormity of Hilbert space out of the textbooks, and rub its full, linear, unmodified truth in the face of anyone who denies it. Or if it isn’t the truth, then we want to discover what is the truth.
Many people would say it’s impossible to make the latter pitch, that funders and laypeople would never understand it or buy it. But there’s an $11-billion, 17-mile ring under Geneva that speaks against their cynicism.
Anyway, let me end this “movie review” with an anecdote. The other day a respected colleague of mine—someone who doesn’t normally follow such matters—asked me what I thought about D-Wave. After I’d given my usual spiel, he smiled and said:
“See Scott, but you could imagine scientists of the 1400s saying the same things about Columbus! He had no plan that could survive academic scrutiny. He raised money under the false belief that he could reach India by sailing due west. And he didn’t understand what he’d found even after he’d found it. Yet for all that, it was Columbus, and not some academic critic on the sidelines, who discovered the new world.”
With this one analogy, my colleague had eloquently summarized the case for D-Wave, a case often leveled against me much more verbosely. But I had an answer.
“I accept your analogy!” I replied. “But to me, Columbus and the other conquerors of the Americas weren’t heroes to be admired or emulated. Motivated by gold and spices rather than knowledge, they spread disease, killed and enslaved millions in one of history’s greatest holocausts, and burned the priceless records of the Maya and Inca civilizations so that the world would never even understand what was lost. I submit that, had it been undertaken by curious and careful scientists—or at least people with a scientific mindset—rather than by swashbucklers funded by greedy kings, the European exploration and colonization of the Americas could have been incalculably less tragic.”
The trouble is, when I say things like that, people just laugh at me knowingly. There he goes again, the pie-in-the-sky complexity theorist, who has no idea what it takes to get anything done in the real world. What an amusingly contrary perspective he has.
And that, in the end, is why I think Particle Fever is such an important movie. Through the stories of the people who built the LHC, you’ll see how it really is possible to reach a new continent without the promise of gold or the allure of lies.
You might recall that Shin, Smith, Smolin, and Vazirani posted a widely-discussed preprint a week ago, questioning the evidence for large-scale quantum behavior in the D-Wave machine. Geordie Rose responded here. Tonight, in a Shtetl-Optimized exclusive scoop, I bring you Umesh Vazirani’s response to Geordie’s comments. Without further ado:
Even a cursory reading of our paper will reveal that Geordie Rose is attacking a straw man. Let me quickly outline the main point of our paper and the irrelevance of Rose’s comments:
To date the Boixo et al paper was the only serious evidence in favor of large scale quantum behavior by the D-Wave machine. We investigated their claims and showed that there are serious problems with their conclusions. Their conclusions were based on the close agreement between the input-output data from D-Wave and quantum simulated annealing, and their inability despite considerable effort to find any classical model that agreed with the input-output data. In our paper, we gave a very simple classical model of interacting magnets that closely agreed with the input-output data. We stated that our results implied that “it is premature to conclude that D-Wave machine exhibits large scale quantum behavior”.
Rose attacks our paper for claiming that “D-Wave processors are inherently classical, and can be described by a classical model with no need to invoke quantum mechanics.” A reading of our paper will make it perfectly clear that this is not a claim that we make. We state explicitly “It is worth emphasizing that the goal of this paper is not to provide a classical model for the D-Wave machine, … The classical model introduced here is useful for the purposes of studying the large-scale algorithmic features of the D-Wave machine. The task of finding an accurate model for the D-Wave machine (classical, quantum or otherwise), would be better pursued with direct access, not only to programming the D-Wave machine, but also to its actual hardware.”
Rose goes on to point to a large number of experiments conducted by D-Wave to prove small scale entanglement over 2-8 qubits and criticizes our paper for not trying to model those aspects of D-Wave. But such small scale entanglement properties are not directly relevant to prospects for a quantum speedup. Therefore we were specifically interested in claims about the large scale quantum behavior of D-Wave. There was exactly one such claim, which we duly investigated, and it did not stand up to scrutiny.
This morning, commenter rrtucci pointed me to TIME Magazine’s cover story about D-Wave (yes, in today’s digital media environment, I need Shtetl-Optimized readers to tell me what’s on the cover of TIME…). rrtucci predicted that, soon after reading the article, I’d be hospitalized with a severe stress-induced bleeding ulcer. Undeterred, I grit my teeth, paid the $5 to go behind the paywall, and read the article.
The article, by Lev Grossman, could certainly be a lot worse. If you get to the end, it discusses the experiments by Matthias Troyer’s group, and it makes clear the lack of any practically-relevant speedup today from the D-Wave devices. It also includes a few skeptical quotes:
“In quantum computing, we have to be careful what we mean by ‘utilizing quantum effects,'” says Monroe, the University of Maryland scientist, who’s among the doubters. “This generally means that we are able to store superpositions of information in such a way that the system retains its ‘fuzziness,’ or quantum coherence, so that it can perform tasks that are impossible otherwise. And by that token there is no evidence that the D-Wave machine is utilizing quantum effects.”
One of the closest observers of the controversy has been Scott Aaronson, an associate professor at MIT and the author of a highly influential quantum-computing blog [aww, shucks –SA]. He remains, at best, cautious. “I’m convinced … that interesting quantum effects are probably present in D-Wave’s devices,” he wrote in an email. “But I’m not convinced that those effects, right now, are playing any causal role in solving any problems faster than we could solve them with a classical computer. Nor do I think there’s any good argument that D-Wave’s current approach, scaled up, will lead to such a speedup in the future. It might, but there’s currently no good reason to think so.”
Happily, the quote from me is something that I actually agreed with at the time I said it! Today, having read the Shin et al. paper—which hadn’t yet come out when Grossman emailed me—I might tone down the statement “I’m convinced … that interesting quantum effects are probably present” to something like: “there’s pretty good evidence for quantum effects like entanglement at a ‘local’ level, but at the ‘global’ level we really have no idea.”
Alas, ultimately I regard this article as another victim (through no fault of the writer, possibly) of the strange conventions of modern journalism. Maybe I can best explain those conventions with a quickie illustration:
MAGIC 8-BALL: THE RENEGADE MATH WHIZ WHO COULD CHANGE NUMBERS FOREVER
An eccentric billionaire, whose fascinating hobbies include nude skydiving and shark-taming, has been shaking up the scientific world lately with his controversial claim that 8+0 equals 17 [… six more pages about the billionaire redacted …] It must be said that mathematicians, who we reached for comment because we’re diligent reporters, have tended to be miffed, skeptical, and sometimes even sarcastic about the billionaire’s claims. Not surprisingly, though, the billionaire and his supporters have had some dismissive comments of their own about the mathematicians. So, which side is right? Or is the truth somewhere in the middle? At this early stage, it’s hard for an outsider to say. In the meantime, the raging controversy itself is reason enough for us to be covering this story using this story template. Stay tuned for more!
As shown (for example) by Will Bourne’s story in Inc. magazine, it’s possible for a popular magazine to break out of the above template when covering D-Wave, or at least bend it more toward reality. But it’s not easy.
More detailed comments:
- The article gets off on a weird foot in the very first paragraph, describing the insides of D-Wave’s devices as “the coldest place in the universe.” Err, 20mK is pretty cold, but colder temperatures are routinely achieved in many other physics experiments. (Are D-Wave’s the coldest current, continuously-operating experiments, or something like that?
I dunno: counterexamples, anyone?I’ve learned from experts that they’re not, not even close. I heard from someone who had a bunch of dilution fridges running at 10mK in the lab he was emailing me from…)
- The article jumps enthusiastically into the standard Quantum Computing = Exponential Parallelism Fallacy (the QC=EPF), which is so common to QC journalism that I don’t know if it’s even worth pointing it out anymore (but here I am doing so).
- Commendably, the article states clearly that QCs would offer speedups only for certain specific problems, not others; that D-Wave’s devices are designed only for adiabatic optimization, and wouldn’t be useful (e.g.) for codebreaking; and that even for optimization, “D-Wave’s hardware isn’t powerful enough or well enough understood to show serious quantum speedup yet.” But there’s a crucial further point that the article doesn’t make: namely, that we have no idea yet whether adiabatic optimization is something where quantum computers can give any practically-important speedup. In other words, even if you could implement adiabatic optimization perfectly—at zero temperature, with zero decoherence—we still don’t know whether there’s any quantum speedup to be had that way, for any of the nifty applications that the article mentions: “software design, tumor treatments, logistical planning, the stock market, airlines schedules, the search for Earth-like planets in other solar systems, and in particular machine learning.” In that respect, adiabatic optimization is extremely different from (e.g.) Shor’s factoring algorithm or quantum simulation: things where we know how much speedup we could get, at least compared to the best currently-known classical algorithms. But I better stop now, since I feel myself entering an infinite loop (and I didn’t even need the adiabatic algorithm to detect it).
Update (Jan. 23): Daniel Lidar, one of the authors of the “Defining and detecting…” paper, was kind enough to email me his reactions to this post. While he thought the post was generally a “very nice summary” of their paper, he pointed out one important oversight in my discussion. Ironically, this oversight arose from my desire to bend over backwards to be generous to D-Wave! Specifically, I claimed that there were maybe ~10% of randomly-chosen 512-qubit problem instances on which the D-Wave Two slightly outperformed the simulated annealing solver (compared to ~75% where simulated annealing outperformed the D-Wave Two), while also listing several reasons (such as the minimum annealing time, and the lack of any characterization of the “good” instances) why that “speedup” is likely to be entirely an artifact. I obtained the ~10% and ~75% figures by eyeballing Figure 7 in the paper, and looking at which quantiles were just above and just below the 100 line when N=512.
However, I neglected to mention that even the slight “speedup” on ~10% of instances, only appears when one looks at the “quantiles of ratio”: in other words, when one plots the probability distribution of [Simulated annealing time / D-Wave time] over all instances, and then looks at (say) the ~10% of the distribution that’s best for the D-Wave machine. The slight speedup disappears when one looks at the “ratio of quantiles”: that is, when one (say) divides the amount of time that simulated annealing needs to solve its best 10% of instances, by the amount of time that the D-Wave machine needs to solve its best 10%. And Rønnow et al. give arguments in their paper that ratio of quantiles is probably the more relevant performance comparison than quantiles of ratio. (Incidentally, the slight speedup on a few instances also only appears for certain values of the parameter r, which controls how many possible settings there are for each coupling. Apparently it appears for r=1, but disappears for r=3 and r=7—thereby heightening one’s suspicion that we’re dealing with an artifact of the minimum annealing time or something like that, rather than a genuine speedup.)
There’s one other important point in the paper that I didn’t mention: namely, all the ratios of simulated annealing time to D-Wave time are normalized by 512/N, where N is the number of spins in the instance being tested. In this way, one eliminates the advantages of the D-Wave machine that come purely from its parallelism (which has nothing whatsoever to do with “quantumness,” and which could easily skew things in D-Wave’s favor if not controlled for), while still not penalizing the D-Wave machine in absolute terms.
A few days ago, a group of nine authors (Troels Rønnow, Zhihui Wang, Joshua Job, Sergio Boixo, Sergei Isakov, David Wecker, John Martinis, Daniel Lidar, and Matthias Troyer) released their long-awaited arXiv preprint Defining and detecting quantum speedup, which contains the most thorough performance analysis of the D-Wave devices to date, and which seems to me to set a new standard of care for any future analyses along these lines. Notable aspects of the paper: it uses data from the 512-qubit machine (a previous comparison had been dismissed by D-Wave’s supporters because it studied the 128-qubit model only); it concentrates explicitly from the beginning on comparisons of scaling behavior between the D-Wave devices and comparable classical algorithms, rather than getting “sidetracked” by other issues; and it includes authors from both USC and Google’s Quantum AI Lab, two places that have made large investments in D-Wave’s machines and have every reason to want to see them succeed.
Let me quote the abstract in full:
The development of small-scale digital and analog quantum devices raises the question of how to fairly assess and compare the computational power of classical and quantum devices, and of how to detect quantum speedup. Here we show how to define and measure quantum speedup in various scenarios, and how to avoid pitfalls that might mask or fake quantum speedup. We illustrate our discussion with data from a randomized benchmark test on a D-Wave Two device with up to 503 qubits. Comparing the performance of the device on random spin glass instances with limited precision to simulated classical and quantum annealers, we find no evidence of quantum speedup when the entire data set is considered, and obtain inconclusive results when comparing subsets of instances on an instance-by-instance basis. Our results for one particular benchmark do not rule out the possibility of speedup for other classes of problems and illustrate that quantum speedup is elusive and can depend on the question posed.
Since the paper is exceedingly well-written, and since I have maybe an hour before I’m called back to baby duty, my inclination is simply to ask people to RTFP rather than writing yet another long blog post. But maybe there are four points worth calling attention to:
- The paper finds, empirically, that the time needed to solve random size-N instances of the quadratic binary optimization (QUBO) problem on D-Wave’s Chimera constraint graph seems to scale like exp(c√N) for some constant c—and that this is true regardless of whether one attacks the problem using the D-Wave Two, quantum Monte Carlo (i.e., a classical algorithm that tries to mimic the native physics of the machine), or an optimized classical simulated annealing code. Notably, exp(c√N) is just what one would have predicted from theoretical arguments based on treewidth; and the constant c doesn’t appear to be better for the D-Wave Two than for simulated annealing.
- The last sentence of the abstract (“Our results … do not rule out the possibility of speedup for other classes of problems”) is, of course, the reed on which D-Wave’s supporters will now have to hang their hopes. But note that it’s unclear what experimental results could ever “rule out the possibility of speedup for other classes of problems.” (No matter how many wrong predictions a psychic has made, the possibility remains that she’d be flawless at predicting the results of Croatian ping-pong tournaments…) Furthermore, like with previous experiments, the instances tested all involved finding ground states for random coupling configurations of the D-Wave machine’s own architecture. In other words, this was a set of instances where one might have thought, a priori, that the D-Wave machine would have an immense home-field advantage. Thus, one really needs to look more closely, to see whether there’s any positive evidence for an asymptotic speedup by the D-Wave machine.
- Here, for D-Wave supporters, the biggest crumb the paper throws is that, if one considers only the ~10% of instances on which the D-Wave machine does best, then the machine does do slightly better on those instances than simulated annealing does. (Conversely, simulated annealing does better than the D-Wave machine on the ~75% of instances on which it does best.) Unfortunately, no one seems to know how to characterize the instances on which the D-Wave machine will do best: one just has to try it and see what happens! And of course, it’s extremely rare that two heuristic algorithms will succeed or fail on exactly the same set of instances: it’s much more likely that their performances will be correlated, but imperfectly. So it’s unclear, at least to me, whether this finding represents anything other than the “noise” that would inevitably occur even if one classical algorithm were pitted against another one.
- As the paper points out, there’s also a systematic effect that biases results in the D-Wave Two’s favor, if one isn’t careful. Namely, the D-Wave Two has a minimum annealing time of 20 microseconds, which is often greater than the optimum annealing time, particularly for small instance sizes. The effect of that is artificially to increase the D-Wave Two’s running time for small instances, and thereby make its scaling behavior look better than it really is. The authors say they don’t know whether even the D-Wave Two’s apparent advantage for its “top 10% of instances” will persist after this effect is fully accounted for.
Those seeking something less technical might want to check out an excellent recent article in Inc. by Will Bourne, entitled “D-Wave’s dream machine” (“D-Wave thinks it has built the first commercial quantum computer. Mother Nature has other ideas”). Wisely, Bourne chose not to mention me at all in this piece. Instead, he gradually builds a skeptical case almost entirely on quotes from people like Seth Lloyd and Daniel Lidar, who one might have thought would be more open to D-Wave’s claims. Bourne’s piece illustrates that it is possible for the mainstream press to get the D-Wave story pretty much right, and that you don’t even need a physics background to do so: all you need is a willingness to commit journalism.
Oh. I’d be remiss not to mention that, in the few days between the appearance of this paper and my having a chance to write this post, two other preprints of likely interest to the Shtetl-Optimized commentariat showed up on quant-ph. The first, by a large list of authors mostly from D-Wave, is called Entanglement in a quantum annealing processor. This paper presents evidence for a point that many skeptics (including me) had been willing to grant for some time: namely, that the states generated by the D-Wave machines contain some nonzero amount of entanglement. (Note that, because of a technical property called “stoquasticity,” such entanglement is entirely compatible with the machines continuing to be efficiently simulable on a classical computer using Quantum Monte Carlo.) While it doesn’t address the performance question at all, this paper seems like a perfectly fine piece of science.
From the opposite side of the (eigen)spectrum comes the latest preprint by QC skeptic Michel Dyakonov, entitled Prospects for quantum computing: Extremely doubtful. Ironically, Dyakonov and D-Wave seem to agree completely about the irrelevance of fault-tolerance and other insights from quantum computing theory. It’s just that D-Wave thinks QC can work even without the theoretical insights, whereas Dyakonov thinks that QC can’t work even with the insights. Unless I missed it, there’s no new scientific content in Dyakonov’s article. It’s basically a summary of some simple facts about QC and quantum fault-tolerance, accompanied by sneering asides about how complicated and implausible it all sounds, and how detached from reality the theorists are.
And as for the obvious comparisons to previous “complicated and implausible” technologies, like (say) classical computing, or heavier-than-air flight, or controlled nuclear fission? Dyakonov says that such comparisons are invalid, because they ignore the many technologies proposed in previous eras that didn’t work. What’s striking is how little he seems to care about why the previous technologies failed: was it because they violated clearly-articulated laws of physics? Or because there turned out to be better ways to do the same things? Or because the technologies were simply too hard, too expensive, or too far ahead of their time? Supposing QC to be impossible, which of those is the reason for the impossibility? Since we’re not asking about something “arbitrary” here (like teaching a donkey to read), but rather about the computational power of Nature itself, isn’t it of immense scientific interest to know the reason for QC’s impossibility? How does Dyakonov propose to learn the reason, assuming he concedes that he doesn’t already know it?
(As I’ve said many times, I’d support even the experiments that D-Wave was doing, if D-Wave and its supporters would only call them for what they were: experiments. Forays into the unknown. Attempts to find out what happens when a particular speculative approach is thrown at NP-hard optimization problems. It’s only when people obfuscate the results of those experiments, in order to claim something as “commercially useful” that quite obviously isn’t yet, that they leave the realm of science, and indeed walk straight into the eager jaws of skeptics like Dyakonov.)
Anyway, since we seem to have circled back to D-Wave, I’d like to end this post by announcing my second retirement as Chief D-Wave Skeptic. The first time I retired, it was because I mistakenly thought that D-Wave had fundamentally changed, and would put science ahead of PR from that point forward. (The truth seems to be that there were, and are, individuals at D-Wave committed to science, but others who remain PR-focused.) This time, I’m retiring for a different reason: because scientists like the authors of the “Defining and detecting” preprint, and journalists like Will Bourne, are doing my job better than I ever did it. If the D-Wave debate were the American Civil War, then my role would be that of the frothy-mouthed abolitionist pamphleteer: someone who repeats over and over points that are fundamentally true, but in a strident manner that serves only to alienate fence-sitters and allies. As I played my ineffective broken record, the Wave Power simply moved from one triumph to another, expanding its reach to Google, NASA, Lockheed Martin, and beyond. I must have looked like a lonely loon on the wrong side of history.
But today the situation is different. Today Honest Abe and his generals (Honest Matthias and his coauthors?) are meeting the Wave Power on the battlefield of careful performance comparisons against Quantum Monte Carlo and simulated annealing. And while the battles might continue all the way to 2000 qubits or beyond, the results so far are not looking great for the Wave Power. The intractability of NP-complete problems—that which we useless, ivory-tower theorists had prophesied years ago, to much derision and laughter—would seem to be rearing its head. So, now that the bombs are bursting and the spins decohering in midair, what is there for a gun-shy pampleteer like myself to do but sit back and watch it all play out?
Well, and maybe blog about it occasionally. But not as “Chief Skeptic,” just as another interested observer.
Merry Christmas! My quantum computing research explained, using only the 1000 most common English wordsTuesday, December 24th, 2013
I study computers that would work in a different way than any computer that we have today. These computers would be very small, and they would use facts about the world that are not well known to us from day to day life. No one has built one of these computers yet—at least, we don’t think they have!—but we can still reason about what they could do for us if we did build them.
How would these new computers work? Well, when you go small enough, you find that, in order to figure out what the chance is that something will happen, you need to both add and take away a whole lot of numbers—one number for each possible way that the thing could happen, in fact. What’s interesting is, this means that the different ways a thing could happen can “kill each other out,” so that the thing never happens at all! I know it sounds weird, but the world of very small things has been known to work that way for almost a hundred years.
So, with the new kind of computer, the idea is to make the different ways each wrong answer could be reached kill each other out (with some of them “pointing” in one direction, some “pointing” in another direction), while the different ways that the right answer could be reached all point in more or less the same direction. If you can get that to happen, then when you finally look at the computer, you’ll find that there’s a very good chance that you’ll see the right answer. And if you don’t see the right answer, then you can just run the computer again until you do.
For some problems—like breaking a big number into its smallest parts (say, 43259 = 181 × 239)—we’ve learned that the new computers would be much, much faster than we think any of today’s computers could ever be. For other problems, however, the new computers don’t look like they’d be faster at all. So a big part of my work is trying to figure out for which problems the new computers would be faster, and for which problems they wouldn’t be.
You might wonder, why is it so hard to build these new computers? Why don’t we have them already? This part is a little hard to explain using the words I’m allowed, but let me try. It turns out that the new computers would very easily break. In fact, if the bits in such a computer were to “get out” in any way—that is, to work themselves into the air in the surrounding room, or whatever—then you could quickly lose everything about the new computer that makes it faster than today’s computers. For this reason, if you’re building the new kind of computer, you have to keep it very, very carefully away from anything that could cause it to lose its state—but then at the same time, you do have to touch the computer, to make it do the steps that will eventually give you the right answer. And no one knows how to do all of this yet. So far, people have only been able to use the new computers for very small checks, like breaking 15 into 3 × 5. But people are working very hard today on figuring out how to do bigger things with the new kind of computer.
In fact, building the new kind of computer is so hard, that some people even believe it won’t be possible! But my answer to them is simple. If it’s not possible, then that’s even more interesting to me than if it is possible! And either way, the only way I know to find out the truth is to try it and see what happens.
Sometimes, people pretend that they already built one of these computers even though they didn’t. Or they say things about what the computers could do that aren’t true. I have to admit that, even though I don’t really enjoy it, I do spend a lot of my time these days writing about why those people are wrong.
Oh, one other thing. Not long from now, it might be possible to build computers that don’t do everything that the new computers could eventually do, but that at least do some of it. Like, maybe we could use nothing but light and mirrors to answer questions that, while not important in and of themselves, are still hard to answer using today’s computers. That would at least show that we can do something that’s hard for today’s computers, and it could be a step along the way to the new computers. Anyway, that’s what a lot of my own work has been about for the past four years or so.
Besides the new kind of computers, I’m also interested in understanding what today’s computers can and can’t do. The biggest open problem about today’s computers could be put this way: if a computer can check an answer to a problem in a short time, then can a computer also find an answer in a short time? Almost all of us think that the answer is no, but no one knows how to show it. Six years ago, another guy and I figured out one of the reasons why this question is so hard to answer: that is, why the ideas that we already know don’t work.
Anyway, I have to go to dinner now. I hope you enjoyed this little piece about the kind of stuff that I work on.