I had a dream

September 14th, 2022

As I slept fitfully, still recovering from COVID, I had one of the more interesting dreams of my life:

I was desperately trying to finish some PowerPoint slides in time to give a talk. Uncharacteristically for me, one of the slides displayed actual code. This was a dream, so nothing was as clear as I’d like, but the code did something vaguely reminiscent of Rosser’s Theorem—e.g., enumerating all proofs in ZFC until it finds the lexicographically first proof or disproof of a certain statement, then branching into cases depending on whether it’s a proof or a disproof. In any case, it was simple enough to fit on one slide.

Suddenly, though, my whole presentation was deleted. Everything was ruined!

One of the developers of PowerPoint happened to be right there in the lecture hall (of course!), so I confronted him with my laptop and angrily demanded an explanation. He said that I must have triggered the section of Microsoft Office that tries to detect and prevent any discussion of logical paradoxes that are too dangerous for humankind—the ones that would cause people to realize that our entire universe is just an illusion, a sandbox being run inside an AI, a glitch-prone Matrix. He said it patronizingly, as if it should’ve been obvious: “you and I both know that the Paradoxes are not to be talked about, so why would you be so stupid as to put one in your presentation?”

My reaction was to jab my finger in the guy’s face, shove him, scream, and curse him out. At that moment, I wasn’t concerned in the slightest about the universe being an illusion, or about glitches in the Matrix. I was concerned about my embarrassment when I’d be called in 10 minutes to give my talk and would have nothing to show.

My last thought, before I woke with a start, was to wonder whether Greg Kuperberg was right and I should give my presentations in Beamer, or some other open-source software, and then I wouldn’t have had this problem.

A coda: I woke a bit after 7AM Central and started to write this down. But then—this is now real life (!)—I saw an email saying that a dozen people were waiting for me in a conference room in Europe for an important Zoom meeting. We’d gotten the time zones wrong; I’d thought that it wasn’t until 8AM my time. If not for this dream causing me to wake up, I would’ve missed the meeting entirely.

What I’ve learned from having COVID

September 4th, 2022
  1. The same thing Salman Rushdie learned: either you spend your entire life in hiding, or eventually it’ll come for you. Years might pass. You might emerge from hiding once, ten times, a hundred times, be fine, and conclude (emotionally if not intellectually) that the danger must now be over, that if it were going to come at all then it already would have, that maybe you’re even magically safe. But this is just the nature of a Poisson process: 0, 0, 0, followed by 1.
  2. First comes the foreboding (in my case, on the flight back home from the wonderful CQIQC meeting in Toronto)—“could this be COVID?”—the urge to reassure yourself that it isn’t, the premature relief when the test is negative. Only then, up to a day later, comes the second vertical line on the plastic cartridge.
  3. I’m grateful for the vaccines, which have up to a 1% probability of having saved my life. My body was as ready for this virus as my brain would’ve been for someone pointing a gun at my head and demanding to know a proof of the Karp-Lipton Theorem. All the same, I wish I also could’ve taken a nasal vaccine, to neutralize the intruder at the gate. Through inaction, through delays, through safetyism that’s ironically caused millions of additional deaths, the regulatory bureaucracies of the US and other nations have a staggering amount to answer for.
  4. Likewise, Paxlovid should’ve been distributed like candy, so that everyone would have a supply and could start the instant they tested positive. By the time you’re able to book an online appointment and send a loved one to a pharmacy, a night has likely passed and the Paxlovid is less effective.
  5. By the usual standards of a cold, this is mild. But the headaches, the weakness, the tiredness … holy crap the tiredness. I now know what it’s like to be a male lion or a hundred-year-old man, to sleep for 20 hours per day and have that feel perfectly appropriate and normal. I can only hope I won’t be one of the long-haulers; if I were, this could be the end of my scientific career. Fortunately the probability seems small.
  6. You can quarantine in your bedroom, speak to your family only through the door, have meals passed to you, but your illness will still cast a penumbra on everyone around you. Your spouse will be stuck watching the kids alone. Other parents won’t let their kids play with your kids … and you can’t blame them; you’d do the same in their situation.
  7. It’s hard to generalize from a sample size of 1 (or 2 if you count my son Daniel, who recovered from a thankfully mild case half a year ago). Readers: what are your COVID stories?

Win a $250,000 Scott Aaronson Grant for Advanced Precollege STEM Education!

September 1st, 2022

Back in January, you might recall, Skype cofounder Jaan Tallinn’s Survival and Flourishing Fund (SFF) was kind enough to earmark $200,000 for me to donate to any charitable organizations of my choice. So I posted a call for proposals on this blog. You “applied” to my “foundation” by simply sending me an email, or leaving a comment on this blog, with a link to your organization’s website and a 1-paragraph explanation of what you wanted the grant for, and then answering any followup questions that I had.

After receiving about 20 awesome proposals in diverse areas, in the end I decided to split the allotment among organizations around the world doing fantastic, badly-needed work in math and science enrichment at the precollege level. These included Canada/USA Mathcamp, AddisCoder, a magnet school in Maine, a math circle in Oregon, a math enrichment program in Ghana, and four others. I chose to focus on advanced precollege STEM education both because I have some actual knowledge and experience there, and because I wanted to make a strong statement about an underfunded cause close to my heart that’s recently suffered unjust attacks.

To quote the immortal Carl Sagan, from shortly before his death:

[C]hildren with special abilities and skills need to be nourished and encouraged. They are a national treasure. Challenging programs for the “gifted” are sometimes decried as “elitism.” Why aren’t intensive practice sessions for varsity football, baseball, and basketball players and interschool competition deemed elitism? After all, only the most gifted athletes participate. There is a self-defeating double standard at work here, nationwide.

Anyway, the thank-you notes from the programs I selected were some of the most gratifying emails I’ve ever received.

But wait, it gets better! After reading about the Scott Aaronson Speculation Grants on this blog, representatives from a large, reputable family foundation contacted me to say that they wanted to be involved too. This foundation, which wishes to remain anonymous at this stage although not to the potential grant recipient, intends to make a single US$250,000 grant in the area of advanced precollege STEM education. They wanted my advice on where their grant should go.

Of course, I could’ve simply picked one of the same wonderful organizations that SFF and I helped in the first round. On reflection, though, I decided that it would be more on the up-and-up to issue a fresh call for proposals.

So: do you run a registered 501(c)(3) nonprofit dedicated to advanced precollege STEM education? If so, email me or leave a comment here by Friday, September 9, telling me a bit about what your organization does and what more it could do with an extra $250K. Include a rough budget, if that will help convince me that you can actually make productive use of that amount, that it won’t just sit in your bank account. Organizations that received a Scott Aaronson Speculation Grant the last time are welcome to reapply; newcomers are also welcome.

I’ll pass up to three finalists along to the funder, which will then make a final decision as to the recipient. The funder will be directly in touch with the potential grantee(s) and will proceed with its intake, review and due diligence process.

We expect to be able to announce a recipient on or around October 24. Can’t wait to see what people come up with!

My Quantum Information Science II Lecture Notes: The wait is over!

August 31st, 2022

Here they are [PDF].

They’re 155 pages of awesome—for a certain extremely specific definition of “awesome”—which I’m hereby offering to the world free of charge (for noncommercial use only, of course). They cover material that I taught, for the first time, in my Introduction to Quantum Information Science II undergrad course at UT Austin in Spring 2022.

The new notes pick up exactly where my older QIS I lecture notes left off, and they presuppose familiarity with the QIS I material. So, if you’re just beginning your quantum information journey, then please start with my QIS I notes, which presuppose only linear algebra and a bit of classical algorithms (e.g., recurrence relations and big-O notation), and which self-containedly explain all the rules of QM, moving on to (e.g.) quantum circuits, density matrices, entanglement entropy, Wiesner’s quantum money, QKD, quantum teleportation, the Bell inequality, interpretations of QM, the Shor 9-qubit code, and the algorithms of Deutsch-Jozsa, Bernstein-Vazirani, Simon, Shor, and Grover. Master all that, and you’ll be close to the quantum information research frontier of circa 1996.

My new QIS II notes cover a bunch of topics, but the main theme is “perspectives on quantum computing that go beyond the bare quantum circuit model, and that became increasingly central to the field from the late 1990s onwards.” Thus, it covers:

  • Hamiltonians, ground states, the adiabatic algorithm, and the universality of adiabatic QC
  • The stabilizer formalism, the 1996 Gottesman-Knill Theorem on efficient classical simulation of stabilizer QC, my and Gottesman’s 2004 elaborations, boosting up to universality via “magic states,” transversal codes, and the influential 2016 concept of stabilizer rank
  • Bosons and fermions: the formalism of Fock space and of creation and annihilation operators, connection to the permanents and determinants of matrices, efficient classical simulation of free fermionic systems (Valiant’s 2002 “matchcircuits”), the 2001 Knill-Laflamme-Milburn (KLM) theorem on universal optical QC, BosonSampling and its computational complexity, and the current experimental status of BosonSampling
  • Cluster states, Raussendorf and Briegel’s 2000 measurement-based quantum computation (MBQC), and Gottesman and Chuang’s 1999 “gate teleportation” trick
  • Matrix product states, and Vidal’s 2003 efficient classical simulation of “slightly entangled” quantum computations

Extra bonus topics include:

  • The 2007 Broadbent-Fitzsimons-Kashefi (BFK) protocol for blind and authenticated QC; brief discussion of later developments including Reichardt-Unger-Vazirani 2012 and Mahadev 2018
  • Basic protocols for quantum state tomography
  • My 2007 work on PAC-learnability of quantum states
  • The “dessert course”: the black hole information problem, and the Harlow-Hayden argument on the computational hardness of decoding Hawking radiation

Master all this, and hopefully you’ll have the conceptual vocabulary to understand a large fraction of what people in quantum computing and information care about today, how they now think about building scalable QCs, and what they post to the quant-ph arXiv.

Note that my QIS II course is complementary to my graduate course on quantum complexity theory, for which the lecture notes are here. There’s very little overlap between the two (and even less overlap between QIS II and Quantum Computing Since Democritus).

The biggest, most important topic related to the QIS II theme that I didn’t cover was topological quantum computing. I’d wanted to, but it quickly became clear that topological QC begs for a whole course of its own, and that I had neither the time nor the expertise to do it justice. In retrospect, I do wish I’d at least covered the Kitaev surface code.

Crucially, these lecture notes don’t represent my effort alone. I worked from draft scribe notes prepared by the QIS II students, who did a far better job than I had any right to expect (including creating the beautiful figures). My wonderful course TA and PhD student Daniel Liang, along with students Ethan Tan, Samuel Ziegelbein, and Steven Han, then assembled everything, fixed numerous errors, and compiled the bibliography. I’m grateful to all of them. At the last minute, we had a LaTeX issue that none of us knew how to fix—but, in response to a plea, Shtetl-Optimized reader Pablo Cingolani generously volunteered to help, completed the work by the very next day (I’d imagined it taking a month!), and earned a fruit basket from me in gratitude.

Anyway, let me know of any mistakes you find! We’ll try to fix them.

Busy Beaver Updates: Now Even Busier

August 30th, 2022

Way back in the covid-filled summer of 2020, I wrote a survey article about the ridiculously-rapidly-growing Busy Beaver function. My survey then expanded to nearly twice its original length, with the ideas, observations, and open problems of commenters on this blog. Ever since, I’ve felt a sort of duty to blog later developments in BusyBeaverology as well. It’s like, I’ve built my dam, I’ve built my lodge, I’m here in the pond to stay!

So without further ado:

  • This May, Pavel Kropitz found a machine demonstrating that $$ BB(6) \ge 10^{10^{10^{10^{10^{10^{10^{10^{10^{10^{10^{10^{10^{10^{10}}}}}}}}}}}}}} $$ (15 times)—thereby blasting through his own 2010 record, that BB(6)≥1036,534. Or, for those tuning in from home: Kropitz constructed a 6-state, 2-symbol, 1-tape Turing machine that runs for at least the above number of steps, when started on an initially blank tape, and then halt. The machine was analyzed and verified by Pascal Michel, the modern keeper of Busy Beaver lore. In my 2020 survey, I’d relayed an open problem posed by my then 7-year-old daughter Lily: namely, what’s the first n such that BB(n) exceeds A(n), the nth value of the Ackermann function? All that’s been proven is that this n is at least 5 and at most 18. Kropitz and Michel’s discovery doesn’t settle the question—titanic though it is, the new lower bound on BB(6) is still less than A(6) (!!)—but in light of this result, I now strongly conjecture that the crossover happens at either n=6 or n=7. Huge congratulations to Pavel and Pascal!
  • Tristan Stérin and Damien Woods wrote to tell me about a new collaborative initiative they’ve launched called BB Challenge. With the participation of other leading figures in the neither-large-nor-sprawling Busy Beaver world, Tristan and Damien are aiming, not only to pin down the value of BB(5)—proving or disproving the longstanding conjecture that BB(5)=47,176,870—but to do so in a formally verified way, with none of the old ambiguities about which Turing machines have or haven’t been satisfactorily analyzed. In my survey article, I’d passed along a claim that, of all the 5-state machines, only 25 remained to be analyzed, to understand whether or not they run forever—the “default guess” being that they all do, but that proving it for some of them might require fearsomely difficult number theory. With their more formal and cautious approach, Tristan and Damien still count 1.5 million (!) holdout machines, but they hope to cut down that number extremely rapidly. If you’re feeling yourself successfully nerd-sniped, please join the quest and help them out!

That Financial Times QC skepticism piece

August 29th, 2022

Several people have asked me to comment about a Financial Times opinion piece entitled The Quantum Computing Bubble (subtitle: “The industry has yet to demonstrate any real utility, despite the fanfare, billions of VC dollars and three Spacs”) (archive link). The piece is purely deflationary—not a positive word in it—though it never goes so far as to suggest that QC is blocked by any Gil-Kalai-like fundamental principle, nor does it even evince curiosity about that question.

As it happens, the author, physicist Nikita Gourianov, had emailed me a few days ago with some nice words about my own skeptical efforts on Shtetl-Optimized, and a request for comment on his article. So, as a way to get back into blogging after a 2-week hiatus, I figured I’d share my respoinse.


Hi Nikita,

Thanks for the kind words about my blog, and for your piece, which I just read.  There’s a great deal of truth in what you write, but I also take issue with a few points.  You say:

A convincing strategy for overcoming these errors has not yet been demonstrated, making it unclear as to when — if ever — it will become possible to build a large-scale, fault-tolerant quantum computer.

In one sense this is tautologically true — the only fully convincing and clear demonstration that something is possible is to do it, as with the Wright brothers or the Trinity nuclear test.  In other sense, though, we’ve known the “strategy” since the 1990s.  It’s just that the fault-tolerance theorem called for gate fidelities 5-6 orders of magnitude better than anything achievable at the time.  In the 25 years since, about 3 of those orders of magnitude have been achieved, so it doesn’t take any great imagination to foresee that the remainder could be as well.  A layperson reading your piece might not understand this.

As for applications, my position has always been that if there were zero applications, it would still be at least as scientifically important to try to build QCs as it was to build the LHC, LIGO, or the James Webb telescope.  If there are real applications, such as simulating chemical dynamics, or certifiable randomness — and there very well might be — then those are icing on the cake.  This, of course, radically differs from the vision that now gets presented to investors and the press (hence all the railing on my blog!), but it also differs from what a reader of your piece would take away.

Anyway, thanks again for sharing!

Best,
Scott

Summer 2022 Quantum Supremacy Updates

August 13th, 2022

Update: We’re now finalizing the lecture notes—basically, a textbook—for the brand-new Quantum Information Science II course that I taught this past spring! The notes will be freely shared on this blog. But the bibliographies for the various lectures need to be merged, and we don’t know how. Would any TeXpert like to help us, in exchange for a generous acknowledgment? A reader named Pablo Cingolani has now graciously taken care of this. Thanks so much, Pablo!


I returned last week from the NSF Workshop on Quantum Advantage and Next Steps at the University of Chicago. Thanks so much to Chicago CS professor (and my former summer student) Bill Fefferman and the other organizers for making this workshop a reality. Given how vividly I remember the situation 12 years ago, when the idea of sampling-based quantum supremacy was the weird obsession of me and a few others, it was particularly special to attend a workshop on the topic with well over a hundred participants, some in-person and some on Zoom, delayed by covid but still excited by the dramatic experimental progress of the past few years.

Of course there’s a lot still to do. Many of the talks drew an exclamation point on something I’ve been saying for the past couple years: that there’s an urgent need for better quantum supremacy experiments, which will require both theoretical and engineering advances. The experiments by Google and USTC and now Xanadu represent a big step forward for the field, but since they started being done, the classical spoofing attacks have also steadily improved, to the point that whether “quantum computational supremacy” still exists depends on exactly how you define it.

Briefly: if you measure by total operations, energy use, or CO2 footprint, then probably yes, quantum supremacy remains. But if you measure by number of seconds, then it doesn’t remain, not if you’re willing to shell out for enough cores on AWS or your favorite supercomputer. And even the quantum supremacy that does remain might eventually fall to, e.g., further improvements of the algorithm due to Gao et al. For more details, see, e.g., the now-published work of Pan, Chen, and Zhang, or this good popular summary by Adrian Cho for Science.

If the experimentalists care enough, they could easily regain the quantum lead, at least for a couple more years, by (say) repeating random circuit sampling with 72 qubits rather than 53-60, and hopefully circuit depth of 30-40 rather than just 20-25. But the point I made in my talk was that, as long as we remain in the paradigm of sampling experiments that take ~2n time to verify classically and also ~2n time to spoof classically (where n is the number of qubits), all quantum speedups that we can achieve will be fragile and contingent, however interesting and impressive. As soon as we go way beyond what classical computers can keep up with, we’ve also gone way beyond where classical computers can check what was done.

I argued that the only solution to this problem is to design new quantum supremacy experiments: ones where some secret has been inserted in the quantum circuit that lets a classical computer efficiently verify the results. The fundamental problem is that we need three properties—

  1. implementability on near-term quantum computers,
  2. efficient classical verifiability, and
  3. confidence that quantum computers have a theoretical asymptotic advantage,

and right now we only know how to get any two out of the three. We can get 1 and 2 with QAOA and various other heuristic quantum algorithms, 1 and 3 with BosonSampling and Random Circuit Sampling, or 2 and 3 with Shor’s algorithm or Yamakawa-Zhandry or recent interactive protocols. To get all three, there are three obvious approaches:

  1. Start with the heuristic algorithms and find a real advantage from them,
  2. Start with BosonSampling or Random Circuit Sampling or the like and figure out how to make them efficiently verifiable classically, or
  3. Start with protocols like Kahanamoku-Meyer et al.’s and figure out how to run them on near-term devices.

At the Chicago workshop, I’d say that the most popular approach speakers talked about was 3, although my own focus was more on 2.

Anyway, until a “best-of-all-worlds” quantum supremacy experiment is discovered, there’s plenty to do in the meantime: for example, better understand the classical hardness of spoofing Random Circuit Sampling with a constant or logarithmic number of gate layers. Understand the best that classical algorithms can do to spoof the Linear Cross-Entropy Benchmark for BosonSampling, and build on Grier et al. to understand the power of BosonSampling with a linear number of modes.

I’ll be flying back with my family to Austin today after seven weeks at the Jersey shore, but I’ll try to field any questions about the state of quantum supremacy in general or this workshop in particular in the comments!

Updatez

August 2nd, 2022
  1. On the IBM Qiskit blog, there’s an interview with me about the role of complexity theory in the early history of quantum computing. Not much new for regular readers, but I’m very happy with how it came out—thanks so much to Robert Davis and Olivia Lanes for making it happen! My only quibble is with the sketch of my face, which might create the inaccurate impression that I no longer have teeth.
  2. Boaz Barak pointed me to a Twitter thread of DALL-E paintings of people using quantum computers, in the styles of many of history’s famous artists. While the motifs are unsurprising (QCs look like regular computers but glowing, or maybe like giant glowing atoms), highly recommended as another demonstration of the sort of thing DALL-E does best.
  3. Dan Spielman asked me to announce that the National Academy of Sciences is seeking nominations for the Held Prize in combinatorial and discrete optimization. The deadline is October 3.
  4. I’m at the NSF Workshop on Quantum Advantage and Next Steps at the University of Chicago. My talk yesterday was entitled “Verifiable Quantum Advantage: What I Hope Will Be Done” (yeah yeah, I decided to call it “advantage” rather than “supremacy” in deference to the name of the workshop). My PowerPoint slides are here. Meanwhile, this morning was the BosonSampling session. The talk by Chaoyang Lu, leader of USTC’s experimental BosonSampling effort, was punctuated by numerous silly memes and videos, as well as the following striking sentence: “only by putting the seven dragon balls together can you unlock the true quantum computational power.”
  5. Gavin Leech lists and excerpts his favorite writings of mine over the past 25 years, while complaining that I spend “a lot of time rebutting fleeting manias” and “obsess[ing] over political flotsam.”

Juris Hartmanis (1928-2022): Guest post by Ryan Williams

July 30th, 2022

Scott’s Introduction

Juris Hartmanis — one of the founding figures of theoretical computer science, winner of the Turing Award, cofounder of the Cornell computer science department (of which I’m an alumnus), cofounder of the Conference on Computational Complexity or CCC (which I just attended), PhD adviser to many of the leading complexity theorists — has passed away at age 94.

Scientifically, Hartmanis will be remembered as long as our field exists for several contributions.  First and foremost, his 1965 proof, with Richard Stearns, of the time and space hierarchy theorems, which adapt Turing’s undecidability theorems to show that there exist computable problems that are arbitrarily hard (and thus, if you like, that the new field of computational complexity theory would have a subject matter).  Second, his and Berman’s investigation, in the 1970s, of the detailed structure of NP-complete problems (are they “paddable”? can they be sparse? are all NP-complete sets polynomial-time isomorphic?  or as we now believe, are they not?), which helped start the whole area of “structural complexity theory” (the original subject matter of the CCC conference).  Third, his investigations of logic and complexity theory, including whether problems like P vs. NP could be independent of the axioms of set theory, and the relations of that question to relativization and oracles.

As this memorial post by Richard Lipton and Ken Regan points out, some of Hartmanis’s most important contributions are so basic that it feels weird today even to mention them explicitly: the use of Turing machines to model computational complexity (!).  The study of complexity via “complexity classes,” consisting of all problems solvable within a given resource bound. The whole Complexity Zoo could’ve been renamed Jurisic Park.

One of my regrets in life is that I didn’t get to know Hartmanis well when I was an undergrad at Cornell.  (This was a stage of my life when I was still intimidated by my professors, or hyper-mega-intimidated if they were Juris Hartmanis.)  I actually conversed with him more after I’d graduated and returned for visits.  He was so considerate and kind, almost grandfatherly, that I realized how foolish I was not to have sought him out as a student.

There was, however, another undergrad at Cornell at the same time as me, who wasn’t quite as intimidated as I was, and who ended up doing an independent study with Hartmanis, about the possibility of complete problems for NP∩coNP if I remember correctly.  This undergrad’s real goal was to solve the P vs. NP problem, which might sound ridiculous until I tell you that his name was Ryan Williams.  I asked Ryan to share his own memories of Juris, and he’s graciously done so below. You won’t regret reading this. —SA


Juris Hartmanis by Ryan Williams

I am extremely sad that Professor Juris Hartmanis has passed away. He made an enormous impact on my early career, and on my growth as a scientist: arguably, I wouldn’t be a scientist at all without him. He was extraordinarily gentle, inspiring, and encouraging to me.

My story of how I know Professor Hartmanis is really my “origin story” as a theoretical computer scientist. So I’ll tell you a little about the situation before I met him, to give some before/after context.

As a freshman at Cornell learning math and computer science, I became captivated by P vs NP and P vs PSPACE. In my teenage hubris, I planned it out: in the spring I’d take discrete math, fall I’d take the intro to theory of computing, and the following spring I’d take the grad complexity course being taught by Prof. Hartmanis that term. After that, I’d go to grad school in theory, and somewhere along the way tackle P vs NP. Simple enough…

I did fine in discrete math, but struggled in intro to theory, partly due to the fact that the lectures (and exams) were at 9am. I managed to do well on the final and earned a B+. I began to wonder if my plan was unsound. I went to the instructor and told them of my plan. They recommended that I should not try for grad school, as I didn’t seem to be particularly talented and there were “no jobs in theory”. (Jobs? Who needs jobs?) I asked if my chances of getting in grad school would be improved if I did well in the grad complexity class. They said “maybe”, and that was enough to keep me going.

The vibe in Prof. Hartmanis’ class was amazing. He was exceptionally passionate about teaching complexity. His lectures were a revelation; they were exhilarating. He stayed laser-focused on communicating the heart of the ideas, with brevity and levity as needed to avoid the technical details (often nasty in the case of Turing machines). In the margins of my own class notes, I jotted down countless one-liners and antics. One of my favorite memories is that, when he wanted to be done with a proof and was tired of further questions, he would write his Q.E.D. symbol very large, with a little intimidating devil inside of it, like so:

As he’d often be packing more material in the lecture than time allowed, he joked that he wasn’t responsible for what was said in the last five minutes of class (when he’d rush to cover what remained). He was having so much fun with the material, and he repeatedly showed that one could think about these very deep and complex things very simply. My intuition for complexity grew so fast that the rest of my mathematical education was lifted immeasurably by it.

In response, I began to take my studies very seriously that semester. I showed up to every session of his office hours. I peppered him with questions after class. He was unwaveringly patient, helping me sort out my latest confusion. Eventually my questions turned into actual research problems, which occasionally received interesting answers (mostly already answered in the literature). After the semester ended, I began to schedule weekly meetings with him, discussing anything and everything complexity. He always seemed happy to chat, and during conversations he made the development of my research taste a top priority. He made it clear when what I was saying was interesting to him, and when it wasn’t… and if it wasn’t, I needed to explain why I found it interesting. But I understood that all of this was for my education as a future theoretical computer scientist, which he treated as inevitable.

I don’t know why Professor Hartmanis believed in me. During that period in my life, I felt like nobody else did, and it felt odd that the Turing Award winner was the one who believed the most. On the coattails of his eager recommendation, I was able to attend an REU at DIMACS. Later he was shocked and annoyed when in spite of his letter, I was rejected from every grad school I applied to (I suppose the B+ didn’t help). However, probably owing to Prof. Hartmanis’ stature at the NSF, I was still awarded an NSF grad fellowship. When I told him of the good and bad news, and that I had no Plan B, he immediately picked up the phone and called someone explaining the situation. He hung up and announced “Congratulations Ryan, you have been admitted to the MEng program.” So I spent the next year in Ithaca as an MEng student. He informed me he was retiring, and maybe grad programs are getting skeptical of complexity. Maybe I should try to sneak in by studying something adjacent. He suggested working with Bart Selman on SAT (which I did). My confidence was shaken by the rejections but, seeing how strongly he believed in me, I could not let him down.

He was always full of affirmations for me, with a trademark mix of humor and motivation. After I would report a batch of new observations, he would say something like: “As they say, the biggest pig eats the most potatoes. And you sir, are a very big pig!” After I had a paper accepted to SODA, he declared that I was now a computer scientist. After I had a paper accepted to IJCAI, he declared that I had become a world-famous computer scientist. Prof. Hartmanis remained my strongest champion and loudest cheerleader in research, until I was finally admitted to some grad schools the next time around.

I’m immensely grateful to have known him. Without his faith, I’d have never become a theoretical computer scientist. Without his initial influence, I’d have never been a good one. I’ve been writing entirely through tears; I hope for everyone reading that they too have the chance to impact a young person’s life so profoundly.


SA’s Endnotes

Besides the obituary by Lipton and Regan, see also the obituary by Bill Gasarch. And especially check out Hartmanis’s extraordinary biographical essay from 2015, in which he describes his childhood in Latvia; his father being taken away by the Soviets to be executed when he was 12 years old; his move to America with his mother, where he worked as a steelworker and a butler while he studied at the University of Kansas City; Caltech’s farsighted decision to admit him as a graduate student despite his unusual background; and then the beginnings of computational complexity theory and the rest of his distinguished career.

On black holes, holography, the Quantum Extended Church-Turing Thesis, fully homomorphic encryption, and brain uploading

July 27th, 2022

I promise you: this post is going to tell a scientifically coherent story that involves all five topics listed in the title. Not one can be omitted.

My story starts with a Zoom talk that the one and only Lenny Susskind delivered for the Simons Institute for Theory of Computing back in May. There followed a panel discussion involving Lenny, Edward Witten, Geoffrey Penington, Umesh Vazirani, and your humble shtetlmaster.

Lenny’s talk led up to a gedankenexperiment involving an observer, Alice, who bravely jumps into a specially-prepared black hole, in order to see the answer to a certain computational problem in her final seconds before being ripped to shreds near the singularity. Drawing on earlier work by Bouland, Fefferman, and Vazirani, Lenny speculated that the computational problem could be exponentially hard even for a (standard) quantum computer. Despite this, Lenny repeatedly insisted—indeed, he asked me again to stress here—that he was not claiming to violate the Quantum Extended Church-Turing Thesis (QECTT), the statement that all of nature can be efficiently simulated by a standard quantum computer. Instead, he was simply investigating how the QECTT needs to be formulated in order to be a true statement.

I didn’t understand this, to put it mildly. If what Lenny was saying was right—i.e., if the infalling observer could see the answer to a computational problem not in BQP, or Bounded-Error Quantum Polynomial-Time—then why shouldn’t we call that a violation of the QECTT? Just like we call Shor’s quantum factoring algorithm a likely violation of the classical Extended Church-Turing Thesis, the thesis saying that nature can be efficiently simulated by a classical computer? Granted, you don’t have to die in order to run Shor’s algorithm, as you do to run Lenny’s experiment. But why should such implementation details matter from the lofty heights of computational complexity?

Alas, not only did Lenny never answer that in a way that made sense to me, he kept trying to shift the focus from real, physical black holes to “silicon spheres” made of qubits, which would be programmed to simulate the process of Alice jumping into the black hole (in a dual boundary description). Say what? Granting that Lenny’s silicon spheres, being quantum computers under another name, could clearly be simulated in BQP, wouldn’t this still leave the question about the computational powers of observers who jump into actual black holes—i.e., the question that we presumably cared about in the first place?

Confusing me even further, Witten seemed almost dismissive of the idea that Lenny’s gedankenexperiment raised any new issue for the QECTT—that is, any issue that wouldn’t already have been present in a universe without gravity. But as to Witten’s reasons, the most I understood from his remarks was that he was worried about various “engineering” issues with implementing Lenny’s gedankenexperiment, involving gravitational backreaction and the like. Ed Witten, now suddenly the practical guy! I couldn’t even isolate the crux of disagreement between Susskind and Witten, since after all, they agreed (bizarrely, from my perspective) that the QECTT wasn’t violated. Why wasn’t it?

Anyway, shortly afterward I attended the 28th Solvay Conference in Brussels, where one of the central benefits I got—besides seeing friends after a long COVID absence and eating some amazing chocolate mousse—was a dramatically clearer understanding of the issues in Lenny’s gedankenexperiment. I owe this improved understanding to conversations with many people at Solvay, but above all Daniel Gottesman and Daniel Harlow. Lenny himself wasn’t there, other than in spirit, but I ran the Daniels’ picture by him afterwards and he assented to all of its essentials.

The Daniels’ picture is what I want to explain in this post. Needless to say, I take sole responsibility for any errors in my presentation, as I also take sole responsibility for not understanding (or rather: not doing the work to translate into terms that I understood) what Susskind and Witten had said to me before.


The first thing you need to understand about Lenny’s gedankenexperiment is that it takes place entirely in the context of AdS/CFT: the famous holographic duality between two types of physical theories that look wildly different. Here AdS stands for anti-de-Sitter: a quantum theory of gravity describing a D-dimensional universe with a negative cosmological constant (i.e. hyperbolic geometry), one where black holes can form and evaporate and so forth. Meanwhile, CFT stands for conformal field theory: a quantum field theory, with no apparent gravity (and hence no black holes), that lives on the (D-1)-dimensional boundary of the D-dimensional AdS space. The staggering claim of AdS/CFT is that every physical question about the AdS bulk can be translated into an equivalent question about the CFT boundary, and vice versa, with a one-to-one mapping from states to states and observables to observables. So in that sense, they’re actually the same theory, just viewed in two radically different ways. AdS/CFT originally came out of string theory, but then notoriously “swallowed its parent,” to the point where nowadays, if you go to what are still called “string theory” meetings, you’re liable to hear vastly more discussion of AdS/CFT than of actual strings.

Thankfully, the story I want to tell won’t depend on fine details of how AdS/CFT works. Nevertheless, you can’t just ignore the AdS/CFT part as some technicality, in order to get on with the vivid tale of Alice jumping into a black hole, hoping to learn the answer to a beyond-BQP computational problem in her final seconds of existence. The reason you can’t ignore it is that the whole beyond-BQP computational problem we’ll be talking about, involves the translation (or “dictionary”) between the AdS bulk and the CFT boundary. If you like, then, it’s actually the chasm between bulk and boundary that plays the starring role in this story. The more familiar chasm within the bulk, between the interior of a black hole and its exterior (the two separated by an event horizon), plays only a subsidiary role: that of causing the AdS/CFT dictionary to become exponentially complex, as far as anyone can tell.

Pause for a minute. Previously I led you to believe that we’d be talking about an actual observer Alice, jumping into an actual physical black hole, and whether Alice could see the answer to a problem that’s intractable even for quantum computers in her last moments before hitting the singularity, and if so whether we should take that to refute the Quantum Extended Church-Turing Thesis. What I’m saying now is so wildly at variance with that picture, that it had to be repeated to me about 10 times before I understood it. Once I did understand, I then had to repeat it to others about 10 times before they understood. And I don’t care if people ridicule me for that admission—how slow Scott and his friends must be, compared to string theorists!—because my only goal right now is to get you to understand it.

To say it again: Lenny has not proposed a way for Alice to surpass the complexity-theoretic power of quantum computers, even for a brief moment, by crossing the event horizon of a black hole. If that was Alice’s goal when she jumped into the black hole, then alas, she probably sacrificed her life for nothing! As far as anyone knows, Alice’s experiences, even after crossing the event horizon, ought to continue to be described extremely well by general relativity and quantum field theory (at least until she nears the singularity and dies), and therefore ought to be simulatable in BQP. Granted, we don’t actually know this—you can call it an open problem if you like—but it seems like a reasonable guess.

In that case, though, what beyond-BQP problem was Lenny talking about, and what does it have to do with black holes? Building on the Bouland-Fefferman-Vazirani paper, Lenny was interested in a class of problems of the following form: Alice is given as input a pure quantum state |ψ⟩, which encodes a boundary CFT state, which is dual to an AdS bulk universe that contains a black hole. Alice’s goal is, by examining |ψ⟩, to learn something about what’s inside the black hole. For example: does the black hole interior contain “shockwaves,” and if so how many and what kind? Does it contain a wormhole, connecting it to a different black hole in another universe? If so, what’s the volume of that wormhole? (Not the first question I would ask either, but bear with me.)

Now, when I say Alice is “given” the state |ψ⟩, this could mean several things: she could just be physically given a collection of n qubits. Or, she could be given a gigantic table of 2n amplitudes. Or, as a third possibility, she could be given a description of a quantum circuit that prepares |ψ⟩, say from the all-0 initial state |0n⟩. Each of these possibilities leads to a different complexity-theoretic picture, and the differences are extremely interesting to me, so that’s what I mostly focused on in my remarks in the panel discussion after Lenny’s talk. But it won’t matter much for the story I want to tell in this post.

However |ψ⟩ is given to Alice, the prediction of AdS/CFT is that |ψ⟩ encodes everything there is to know about the AdS bulk, including whatever is inside the black hole—but, and this is crucial, the information about what’s inside the black hole will be pseudorandomly scrambled. In other words, it works like this: whatever simple thing you’d like to know about parts of the bulk that aren’t hidden behind event horizons—is there a star over here? some gravitational lensing over there? etc.—it seems that you could not only learn it by measuring |ψ⟩, but learn it in polynomial time, the dictionary between bulk and boundary being computationally efficient in that case. (As with almost everything else in this subject, even that hasn’t been rigorously proven, though my postdoc Jason Pollack and I made some progress this past spring by proving a piece of it.) On the other hand, as soon as you want to know what’s inside an event horizon, the fact that there are no probes that an “observer at infinity” could apply to find out, seems to translate into the requisite measurements on |ψ⟩ being exponentially complex to apply. (Technically, you’d have to measure an ensemble of poly(n) identical copies of |ψ⟩, but I’ll ignore that in what follows.)

In more detail, the relevant part of |ψ⟩ turns into a pseudorandom, scrambled mess: a mess that it’s plausible that no polynomial-size quantum circuit could even distinguish from the maximally mixed state. So, while in principle the information is all there in |ψ⟩, getting it out seems as hard as various well-known problems in symmetric-key cryptography, if not literally NP-hard. This is way beyond what we expect even a quantum computer to be able to do efficiently: indeed, after 30 years of quantum algorithms research, the best quantum speedup we know for this sort of task is typically just the quadratic speedup from Grover’s algorithm.

So now you understand why there was some hope that Alice, by jumping into a black hole, could solve a problem that’s exponentially hard for quantum computers! Namely because, once she’s inside the black hole, she can just see the shockwaves, or the volume of the wormhole, or whatever, and no longer faces the exponentially hard task of decoding that information from |ψ⟩. It’s as if the black hole has solved the problem for her, by physically instantiating the otherwise exponentially complex transformation between the bulk and boundary descriptions of |ψ⟩.

Having now gotten your hopes up, the next step in the story is to destroy them.


Here’s the fundamental problem: |ψ⟩ does not represent the CFT dual of a bulk universe that contains the black hole with the shockwaves or whatever, and that also contains Alice herself, floating outside the black hole, and being given |ψ⟩ as an input.  Indeed, it’s unclear what the latter state would even mean: how do we get around the circularity in its definition? How do we avoid an infinite regress, where |ψ⟩ would have to encode a copy of |ψ⟩ which would have to encode a copy of … and so on forever? Furthermore, who created this |ψ⟩ to give to Alice? We don’t normally imagine that an “input state” contains a complete description of the body and brain of the person whose job it is to learn the output.

By contrast, a scenario that we can define without circularity is this: Alice is given (via physical qubits, a giant table of amplitudes, an obfuscated quantum circuit, or whatever) a pure quantum state |ψ⟩, which represents the CFT dual of a hypothetical universe containing a black hole.  Alice wants to learn what shockwaves or wormholes are inside the black hole, a problem plausibly conjectured not to have any ordinary polynomial-size quantum circuit that takes copies of |ψ⟩ as input.  To “solve” the problem, Alice sets into motion the following sequence of events:

  1. Alice scans and uploads her own brain into a quantum computer, presumably destroying the original meat brain in the process! The QC represents Alice, who now exists only virtually, via a state |φ⟩.
  2. The QC performs entangling operations on |φ⟩ and |ψ⟩, which correspond to inserting Alice into the bulk of the universe described by |ψ⟩, and then having her fall into the black hole.
  3. Now in simulated form, “Alice” (or so we assume, depending on our philosophical position) has the subjective experience of falling into the black hole and observing what’s inside.  Success! Given |ψ⟩ as input, we’ve now caused “Alice” (for some definition of “Alice”) to have observed the answer to the beyond-BQP computational problem.

In the panel discussion, I now model Susskind as having proposed scenario 1-3, Witten as going along with 1-2 but rejecting 3 or not wanting to discuss it, and me as having made valid points about the computational complexity of simulating Alice’s experience in 1-3, yet while being radically mistaken about what the scenario was (I still thought an actual black hole was involved).

An obvious question is whether, having learned the answer, “Alice” can now get the answer back out to the “real, original” world. Alas, the expectation is that this would require exponential time. Why? Because otherwise, this whole process would’ve constituted a subexponential-time algorithm for distinguishing random from pseudorandom states using an “ordinary” quantum computer! Which is conjectured not to exist.

And what about Alice herself? In polynomial time, could she return from “the Matrix,” back to a real-world biological body? Sure she could, in principle—if, for example, the entire quantum computation were run in reverse. But notice that reversing the computation would also make Alice forget the answer to the problem! Which is not at all a coincidence: if the problem is outside BQP, then in general, Alice can know the answer only while she’s “inside the Matrix.”

Now that hopefully everything is crystal-clear and we’re all on the same page, what can we say about this scenario?  In particular: should it cause us to reject or modify the QECTT itself?


Daniel Gottesman, I thought, offered a brilliant reductio ad absurdum of the view that the simulated black hole scenario should count as a refutation of the QECTT. Well, he didn’t call it a “reductio,” but I will.

For the reductio, let’s forget not only about quantum gravity but even about quantum mechanics itself, and go all the way back to classical computer science.  A fully homomorphic encryption scheme, the first example of which was discovered by Craig Gentry 15 years ago, lets you do arbitrary computations on encrypted data without ever needing to decrypt it.  It has both an encryption key, for encrypting the original plaintext data, and a separate decryption key, for decrypting the final answer.

Now suppose Alice has some homomorphically encrypted top-secret emails, which she’d like to read.  She has the encryption key (which is public), but not the decryption key.

If the homomorphic encryption scheme is secure against quantum computers—as the schemes discovered by Gentry and later researchers currently appear to be—and if the QECTT is true, then Alice’s goal is obviously infeasible: decrypting the data will take her exponential time.

Now, however, a classical version of Lenny comes along, and explains to Alice that she simply needs to do the following:

  1. Upload her own brain state into a classical computer, destroying the “meat” version in the process (who needed it?).
  2. Using the known encryption key, homomorphically encrypt a computer program that simulates (and thereby, we presume, enacts) Alice’s consciousness.
  3. Using the homomorphically encrypted Alice-brain, together with the homomorphically encrypted input data, do the homomorphic computations that simulate the process of Alice’s brain reading the top-secret emails.

The claim would now be that, inside the homomorphic encryption, the simulated Alice has the subjective experience of reading the emails in the clear.  Aha, therefore she “broke” the homomorphic encryption scheme! Therefore, assuming that the scheme was secure even against quantum computers, the QECTT must be false!

According to Gottesman, this is almost perfectly analogous to Lenny’s black hole scenario.  In particular, they share the property that “encryption is easy but decryption is hard.”   Once she’s uploaded her brain, Alice can efficiently enter the homomorphically encrypted world to see the solution to a hard problem, just like she can efficiently enter the black hole world to do the same.  In both cases, however, getting back to her normal world with the answer would then take Alice exponential time.  Note that in the latter case, the difficulty is not so much about “escaping from a black hole,” as it is about inverting the AdS/CFT dictionary.

Going further, we can regard the AdS/CFT dictionary for regions behind event horizons as, itself, an example of a fully homomorphic encryption scheme—in this case, of course, one where the ciphertexts are quantum states.  This strikes me as potentially an important insight about AdS/CFT itself, even if that wasn’t Gottesman’s intention. It complements many other recent connections between AdS/CFT and theoretical computer science, including the view of AdS/CFT as a quantum error-correcting code, and the connection between AdS/CFT and the Max-Flow/Min-Cut Theorem (see also my talk about my work with Jason Pollack).

So where’s the reductio?  Well, when it’s put so starkly, I suspect that not many would regard Gottesman’s classical homomorphic encryption scenario as a “real” challenge to the QECTT.  Or rather, people might say: yes, this raises fascinating questions for the philosophy of mind, but at any rate, we’re no longer talking about physics.  Unlike with (say) quantum computing, no new physical phenomenon is being brought to light that lets an otherwise intractable computational problem be solved.  Instead, it’s all about the user herself, about Alice, and which physical systems get to count as instantiating her.

It’s like, imagine Alice at the computer store, weighing which laptop to buy. Besides weight, battery life, and price, she definitely does care about processing power. She might even consider a quantum computer, if one is available. Maybe even a computer with a black hole, wormhole, or closed timelike curve inside: as long as it gives the answers she wants, what does she care about the innards? But a computer whose normal functioning would (pessimistically) kill her or (optimistically) radically change her own nature, trapping her in a simulated universe that she can escape only by forgetting the computer’s output? Yeah, I don’t envy the computer salesman.

Anyway, if we’re going to say this about the homomorphic encryption scenario, then shouldn’t we say the same about the simulated black hole scenario?  Again, from an “external” perspective, all that’s happening is a giant BQP computation.  Anything beyond BQP that we consider to be happening, depends on adopting the standpoint of an observer who “jumps into the homomorphic encryption on the CFT boundary”—at which point, it would seem, we’re no longer talking about physics but about philosophy of mind.


So, that was the story! I promised you that it would integrally involve black holes, holography, the Quantum Extended Church-Turing Thesis, fully homomorphic encryption, and brain uploading, and I hope to have delivered on my promise.

Of course, while this blog post has forever cleared up all philosophical confusions about AdS/CFT and the Quantum Extended Church-Turing Thesis, many questions of a more technical nature remain. For example: what about the original scenario? can we argue that the experiences of bulk observers can be simulated in BQP, even when those observers jump into black holes? Also, what can we say about the complexity class of problems to which the simulated Alice can learn the answers? Could she even solve NP-complete problems in polynomial time this way, or at least invert one-way functions? More broadly, what’s the power of “BQP with an oracle for applying the AdS/CFT dictionary”—once or multiple times, in one direction or both directions?

Lenny himself described his gedankenexperiment as exploring the power of a new complexity class that he called “JI/poly,” where the JI stands for “Jumping In” (to a black hole, that is). The nomenclature is transparently ridiculous—“/poly” means “with polynomial-size advice,” which we’re not talking about here—and I’ve argued in this post that the “JI” is rather misleading as well. If Alice is “jumping” anywhere, it’s not into a black hole per se, but into a quantum computer that simulates a CFT that’s dual to a bulk universe containing a black hole.

In a broader sense, though, to contemplate these questions at all is clearly to “jump in” to … something. It’s old hat by now that one can start in physics and end up in philosophy: what else is the quantum measurement problem, or the Boltzmann brain problem, or anthropic cosmological puzzles like whether (all else equal) we’re a hundred times as likely to find ourselves in a universe with a hundred times as many observers? More recently, it’s also become commonplace that one can start in physics and end in computational complexity theory: quantum computing itself is the example par excellence, but over the past decade, the Harlow-Hayden argument about decoding Hawking radiation and the complexity = action proposal have made clear that it can happen even in quantum gravity.

Lenny’s new gedankenexperiment, however, is the first case I’ve seen where you start out in physics, and end up embroiled in some of the hardest questions of philosophy of mind and computational complexity theory simultaneously.