Because my mental world was in such upheaval, in that earlier post I took pains to point out one thing that Ambainis et al. *hadn’t* done: namely, they still hadn’t shown any super-quadratic separation between R(f) and Q(f), for any total Boolean function f. (Recall that a *total* Boolean function, f:{0,1}^{n}→{0,1}, is one that’s defined for all 2^{n} possible input strings x∈{0,1}^{n}. Meanwhile, a *partial* Boolean function is one where there’s some promise on x: for example, that x encodes a periodic sequence. When you phrase them in the query complexity model, Shor’s algorithm and other quantum algorithms achieving exponential speedups work only for partial functions, not for total ones. Indeed, a famous result of Beals et al. from 1998 says that D(f)=O(Q(f)^{6}) for all total functions f.)

So, clinging to a slender reed of sanity, I said it “remains at least a plausible conjecture” that, if you insist on a fair comparison—i.e., bounded-error quantum versus bounded-error randomized—then the biggest speedup quantum algorithms can ever give you over classical ones, for total Boolean functions, is the square-root speedup that Grover’s algorithm easily achieves for the n-bit OR function.

Today, I can proudly report that my PhD student, Shalev Ben-David, has refuted *that* conjecture as well. Building on the Göös et al. and Ambainis et al. work, but adding a new twist to it, Shalev has constructed a total Boolean function f such that R(f) grows roughly like Q(f)^{2.5} (yes, that’s Q(f) to the 2.5^{th} power). Furthermore, if a conjecture that Ambainis and I made in our recent “Forrelation” paper is correct—namely, that a problem called “k-fold Forrelation” has randomized query complexity roughly Ω(n^{1-1/k})—then one would get nearly a cubic gap between R(f) and Q(f).

The reason I found this question so interesting is that it seemed obvious to me that, to produce a super-quadratic separation between R and Q, one would need a *fundamentally new kind of quantum algorithm*: one that was unlike Simon’s and Shor’s algorithms in that it worked for total functions, but also unlike Grover’s algorithm in that it didn’t hit some impassable barrier at the square root of the classical running time.

Flummoxing my expectations once again, Shalev produced the super-quadratic separation, but *not* by designing any new quantum algorithm. Instead, he cleverly engineered a Boolean function for which you can use a combination of Grover’s algorithm and the Forrelation algorithm (or any other quantum algorithm that gives a huge speedup for some *partial* Boolean function—Forrelation is just the maximal example), to get an overall speedup that’s a little more than quadratic, while still keeping your Boolean function total. I’ll let you read Shalev’s short paper for the details, but briefly, it once again uses the Göös et al. / Ambainis et al. trick of defining a Boolean function that equals 1 if and only if the input string contains some hidden substructure, and *the hidden substructure also contains a pointer to a “certificate” that lets you quickly verify that the hidden substructure was indeed there.* You can use a super-fast algorithm—let’s say, a quantum algorithm designed for partial functions—to find the hidden substructure assuming it’s there. If you don’t find it, you can simply output 0. But if you *do* find it (or *think* you found it), then you can use the certificate, together with Grover’s algorithm, to confirm that you weren’t somehow misled, and that the substructure really *was* there. This checking step ensures that the function remains total.

Are there further separations to be found this way? Almost certainly! Indeed, Shalev, Robin Kothari, and I have already found some more things (as well as different/simpler proofs of known separations), though nothing quite as exciting as the above.

**Update (July 1):** Ronald de Wolf points out in the comments that this “trust-but-verify” trick, for designing total Boolean functions with unexpectedly low quantum query complexities, was also used in a recent paper by himself and Ambainis (while Ashley Montanaro points out that a similar trick was used even earlier, in a different context, by Le Gall). What’s surprising, you might say, is that it took as long as it did for people to realize how many applications this trick has.

So I’m proud for my country, and I’m thrilled for my gay friends and colleagues and relatives. At the same time, seeing my Facebook page light up with an endless sea of rainbow flags and jeers at Antonin Scalia, there’s something that gnaws at me. To stand up for Alan Turing in 1952 would’ve taken genuine courage. To support gay rights in the 60s, 70s, 80s, even the 90s, took courage. But celebrating a social change when you *know* all your friends will upvote you, more than a decade after the tide of history has made the change unstoppable? It’s fun, it’s righteous, it’s justified, I’m doing it myself. But let’s not kid ourselves by calling it courageous.

Do you want to impress me with your moral backbone? Then go and find a group that almost all of your Facebook friends still consider it okay, even praiseworthy, to despise and mock, for moral failings that either aren’t failings at all or are no worse than the rest of humanity’s. (I promise: once you start looking, it shouldn’t be hard to find.) Then take a public stand for *that* group.

- The edit distance between two strings means the minimum number of insertions, deletions, and replacements needed to convert one string to the other: for example, SHTETL and SHETLAND have an edit distance of 4. Edit distance has major, obvious applications to DNA sequence comparison, as well as plagiarism detection and many other things. There’s a clever dynamic programming algorithm to compute the edit distance between two n-bit strings, but it takes ~n
^{2}time, which is already too slow for many applications. Can you do better? I remember wondering about that 15 years ago, as a beginning grad student taking Richard Karp’s computational biology course. Now Arturs Backurs and Piotr Indyk have shown that, if you can compute edit distance in O(n^{2-ε}) time for any ε>0, then you can also solve CNF-SAT in 2^{cn}time for some c<1, thereby refuting the Strong Exponential Time Hypothesis. For more about this important result, see this*MIT News*article.

- Olivier Temam gave a superb keynote talk about hardware neural networks. His main points were these: implementing neural nets with special-purpose hardware was a faddish idea a few decades ago, but was abandoned once people realized that (a) it didn’t work that great, and (b) more to the point, anything you could do with special-purpose hardware, you could do better and more easily with silicon chips, after waiting just a few years for Moore’s Law to catch up. Today, however, two things have spurred a revival of the idea: firstly, neural nets (renamed “deep learning,” and done with bigger networks and way more training data) are delivering spectacular, state-of-the-art results; and second, transistors have stopped shrinking, so it now makes more sense to think about the few orders-of-magnitude speed improvement that you can get from special-purpose hardware. This would mean organizing computers kind-of, sort-of like the brain is organized, with (for example) memory integrated into the connections between the “neurons” (processing elements), rather than on a separate chip that’s connected to the processor by a bus. On the other hand, Temam also stressed that computer architects shouldn’t slavishly copy the brain: instead, they should simply build the fastest hardware they can to implement the best available machine-learning algorithms, and they should rely on the machine-learning theorists to incorporate whatever broad lessons are to be gleaned from neuroscience (as they’ve done several times in the past).

- Three separate sets of authors (Koppula, Lewko, and Waters; Canetti, Holmgren, Jain, and Vaikuntanathan; and Bitansky, Garg, Lin, Pass, and Telang) independently wrote papers that showed how to achieve “indistinguishability obfuscation” (i.o.) for Turing machines rather than for circuits. For those not in the world of theoretical crypto, i.o. is a hot concept that basically means: obfuscating a program in such a way that no adversary can figure out anything about
*which program you started with*, among all the possible programs that compute the same function in roughly the same amount of time. (On the other hand, the adversary might be able to learn more than she could if merely given a*black box*for the function. And that’s why this kind of obfuscation falls short of the “gold standard,” which was shown to be impossible in general in seminal work by Barak et al.) Recent papers have shown how to achieve the weaker notion of i.o., but they first require converting your program to a Boolean circuit—something that’s absurdly inefficient in practice, and also has the theoretical drawback of producing an obfuscated program whose size grows, not merely with the size of the original, unobfuscated program, but also with the amount of time the original program is supposed to run for. So, the new work gets around that drawback, by cleverly obfuscating a program whose purpose is to compute the “next step function” of the original program, on data that’s itself encrypted. The talk was delivered in “tag team” format, with one representative from each group of authors speaking for 6-7 minutes. Surprisingly, it worked extremely well.

- Laci Babai gave a truly epic hour-long Knuth Prize lecture, basically trying to summarize all of his work over the past 35 years (and related work by others), in 170 or so slides. The talk had not a single word of filler: it was just pure beef, result after result, some of them well-known and seminal (e.g., MIP=NEXP, AM[2]=AM[k], AlmostNP=MA, group membership in NP, group non-membership in AM…) and others obscure little gems. Boaz Barak commented that an entire semester-long course could be taught from the PowerPoint slides. Laci ended the talk by defining the Babai point, and then saying “having made my point, I’m done.”

- Ambainis (yes, the same Ambainis), Filmus and Le Gall had a paper about the limitations of the techniques used to achieve all matrix multiplication algorithms from Coppersmith-Winograd (O(n
^{2.3755})) onward, including those of Stothers 2010 (O(n^{2.3730})), Vassilevska Williams 2012 (O(n^{2.3728642})), and Le Gall 2014 (O(n^{2.3728639})). Their basic conclusion—not surprising, but still nice to establish formally—is that applying more and more massive computer search to the current ideas can’t possibly get you below O(n^{2.308}); new ideas will be needed to push further.

- At the STOC business meeting, there was a long discussion about the proposal to turn STOC into a weeklong “theory festival,” with more plenary talks (including from other fields), possibly more parallel sessions, etc. There were lots of interesting arguments, but alas, I was too tired and jetlagged to remember what they were. (Anyone who
*does*remember is welcome to chime in.)

There are many great things that I haven’t written about—for example, I haven’t even said a word about any of the three best paper talks!—but I’m out of energy right now. Others are more than welcome to share other FCRC highlights in the comments section.

]]>Lots of people have accused me of overusing the word “breakthrough” on this blog. So I ask them: what word *should* I use when a paper comes out that solves not one, not two, but three of the open problems I’ve cared about most for literally half of my life, since I was 17 years old?

Yesterday morning, Andris Ambainis, Kaspars Balodis, Aleksandrs Belovs, Troy Lee, Miklos Santha, and Juris Smotrovs posted a preprint to ECCC in which they give:

(1) A total Boolean function f with roughly a fourth-power separation between its deterministic and bounded-error quantum query complexities (i.e., with D(f)~Q(f)^{4}). This refutes the conjecture, which people have been making since Beals et al.’s seminal work in 1998, that the biggest possible gap is quadratic.

(2) A total Boolean function f with a quadratic separation between its deterministic and randomized query complexities (with D(f)~R_{0}(f)^{2}). This refutes a conjecture of Saks and Wigderson from 1986, that the best possible gap is R_{0}(f)~D(f)^{0.753} (from the recursive AND/OR tree), and shows that the known relation D(f)=O(R_{0}(f)^{2}) is close to tight.

(3) The first total Boolean function f with *any* asymptotic gap between its zero-error and bounded-error randomized query complexities (in particular, with R_{0}(f)~R(f)^{3/2}).

(There are also other new separations—for example, involving exact quantum query complexity and approximate degree as a real polynomial. But the above three are the most spectacular to me.)

In updates to this post (coming soon), I’ll try my best to explain to general readers what D(f), R(f), and so forth *are* (see here for the classic survey of these measures), and I’ll also discuss how Ambainis et al. designed the strange functions f that achieve the separations (though their paper already does a good job of explaining it). For now, I’ll just write the stuff that’s easier to write.

I’m at the Federated Computing Research Conference in Portland, Oregon right now, where yesterday I gave my STOC talk (click here for the PowerPoint slides) about the largest possible separations between R(f) and Q(f) for *partial* Boolean functions f. (That paper is *also* joint work with Andris Ambainis, who has his fingers in many pies, or his queries in many oracles, or something.) Anyway, when I did a practice run of my talk on Monday night, I commented that, of course, for *total* Boolean functions f (those not involving a promise), the largest known gap between R(f) and Q(f) is quadratic, and is achieved when f is the OR function because of Grover’s algorithm.

Then, Tuesday morning, an hour before I was to give my talk, I saw the Ambainis et al. bombshell, which made that comment obsolete. So, being notoriously bad at keeping my mouth shut, I mentioned to my audience that, while it was great that they came all the way to Portland to learn what was new in theoretical computer science, if they wanted *real* news in the subfield I was talking about, they could stop listening to me and check their laptops.

(Having said that, I *have* had a wonderful time at FCRC, and have learned lots of other interesting things—I can do another addendum to the post about FCRC highlights if people want me to.)

Anyway, within the tiny world of query complexity—i.e., the world where I cut my teeth and spent much of my career—the Ambainis et al. paper is sufficiently revolutionary that I feel the need to say what it *doesn’t* do.

First, the paper does not give a better-than-quadratic gap between R(f) and Q(f) (i.e., between bounded-error randomized and quantum query complexities). The quantum algorithms that compute their functions f are still “just” variants of the old standbys, Grover’s algorithm and amplitude amplification. What’s new is that the authors have found functions where you can get the quadratic, Grover speedup between R(f) and Q(f), while *also* getting asymptotic gaps between D(f) and R(f), and between R_{0}(f) and R(f). So, putting it together, you get superquadratic gaps between D(f) and Q(f), and between R_{0}(f) and Q(f). But it remains at least a plausible conjecture that R(f)=O(Q(f)^{2}) for all total Boolean functions f—i.e., if you insist on a “fair comparison,” then the largest known quantum speedup for total Boolean functions remains the Grover one.

Second, as far as I can tell ~~(I might be mistaken)~~ (I’m not), the paper doesn’t give new separations involving certificate complexity or block sensitivity (e.g., between D(f) and bs(f)). So for example, it remains open whether D(f)=O(bs(f)^{2}), and ~~whether C(f)=O(bs(f)~~ (Update: Avishay Tal, in the comments, informs me that the latter conjecture was falsified by Gilmer, Saks, and Srinivasan in 2013. Wow, I’m ^{α}) for some α<2.*really* out of it!)

In the end, achieving these separations didn’t require any sophisticated new mathematical machinery—just finding the right functions, something that could’ve been done back in 1998, had anyone been clever enough. So, where did these bizarre functions f come from? Ambainis et al. directly adapted them from a great recent communication complexity paper by Mika Göös, Toniann Pitassi, and Thomas Watson. But the Göös et al. paper itself could’ve been written much earlier. It’s yet another example of something I’ve seen again and again in this business, how there’s no substitute for just playing around with a bunch of examples.

The highest compliment one researcher can pay another is, “I wish I’d found that myself.” And I do, of course, but having missed it, I’m thrilled that at least I get to be alive for it and blog about it. Huge congratulations to the authors!

**Addendum: What’s this about?**

OK, so let’s say you have a Boolean function f:{0,1}^{n}→{0,1}, mapping n input bits to 1 output bit. Some examples are the OR function, which outputs 1 if any of the n input bits are 1, and the MAJORITY function, which outputs 1 if the majority of them are.

*Query complexity* is the study of how many input bits you need to read in order to learn the value of the output bit. So for example, in evaluating the OR function, if you found a single input bit that was 1, you could stop right there: you’d know that the output was 1, without even needing to look at the remaining bits. In the worst case, however, if the input consisted of all 0s, you’d have to look at all of them before you could be totally sure the output was 0. So we say that the OR function has a deterministic query complexity of n.

In this game, we don’t care about any other resources used by an algorithm, like memory or running time: just how many bits of the input it looks at! There are many reasons why, but the simplest is that, unlike with memory or running time, for many functions *we can actually figure out* how many input bits need to be looked at, without needing to solve anything like P vs. NP. (But note that this can already be nontrivial! For algorithms can often cleverly avoid looking at all the bits, for example by looking at some and then deciding which ones to look at next based on which values they see.)

In general, given a deterministic algorithm A and an n-bit input string x, let D_{A,x} (an integer from 0 to n) be the number of bits of x that A examines when you run it. Then let D_{A} be the maximum of D_{A,x} over all n-bit strings x. Then D(f), or the deterministic query complexity of f, is the minimum of D_{A}, over all algorithms A that correctly evaluate f(x) on every input x.

For example, D(OR) and D(MAJORITY) are both n: in the worst case, you need to read everything. For a more interesting example, consider the 3-bit Boolean function

f(x,y,z) = (not(x) and y) or (x and z).

This function has D(f)=2, even though it depends on all 3 of the input bits. (Do you see why?) In general, even if f depends on n input bits, D(f) could be as small as log_{2}n.

The *bounded-error randomized query complexity*, or R(f), is like D(f), except that now we allow the algorithm to make random choices of which input bit to query, and for each input x, the algorithm only needs to compute f(x) with probability 2/3. (Here the choice of 2/3 is arbitrary; if you wanted the right answer with some larger constant probability, say 99.9%, you could just repeat the algorithm a constant number of times and take a majority vote.) The *zero-error randomized query complexity*, or R_{0}(f), is the variant where the algorithm is allowed to make random choices, but at the end of the day, needs to output the correct f(x) with probability 1.

To illustrate these concepts, consider the three-bit majority function, MAJ(x,y,z). We have D(MAJ)=3, since if a deterministic algorithm queried one bit and got a 0 and queried a second bit and got a 1 (as can happen), it would have no choice but to query the third bit. But for any possible setting of x, y, and z, if we choose which bits to query *randomly*, there’s at least a 1/3 chance that the first two queries will return either two 0s or two 1s—at which point we can stop, with no need to query the third bit. Hence R_{0}(MAJ)≤(1/3)2+(2/3)3=8/3 (in fact it equals 8/3, although we haven’t quite shown that). Meanwhile, R(MAJ), as we defined it, is only 1, since if you just need a 2/3 probability of being correct, you can simply pick x, y, or z at random and output it!

The *bounded-error quantum query complexity*, or Q(f), is the minimum number of queries made by a quantum algorithm for f, which, again, has to output the right answer with probability at least 2/3 for every input x. Here a quantum algorithm makes a “query” by feeding a superposition of basis states, each of the form |i,a,w〉, to a “black box,” which maps each basis state to |i, a XOR x_{i}, w〉, where i is the index of the input bit x_{i} to be queried, a is a 1-qubit “answer register” into which x_{i} is reversibly written, and w is a “workspace” that doesn’t participate in the query. In between two queries, the algorithm can apply any unitary transformation it wants to the superposition of |i,a,w〉’s, as long as it doesn’t depend on x. Finally, some designated qubit is measured to decide whether the algorithm accepts or rejects.

As an example, consider the 2-bit XOR function, XOR(x,y). We have D(XOR)=R_{0}(XOR)=R(XOR)=2, since until you’ve queried both bits, you’ve learned nothing about their XOR. By contrast, Q(XOR)=1, because of the famous Deutsch-Jozsa algorithm.

It’s clear that

0 ≤ Q(f) ≤ R(f) ≤ R_{0}(f) ≤ D(f) ≤ n,

since a quantum algorithm can simulate a randomized one and a randomized one can simulate a deterministic one.

A central question for the field, since these measures were studied in the 1980s or so, has been how far apart these measures can get from each other. If you allow *partial* Boolean functions—meaning that only some n-bit strings, not all of them, are “valid inputs” for which the algorithm needs to return a definite answer—then it’s easy to get *enormous* separations between any two of the measures (indeed, even bigger than exponential), as for example in my recent paper with Andris.

For total functions, by contrast, it’s been known for a long time that these measures can differ by at most polynomial factors:

D(f) = O(R(f)^{3}) (Nisan)

D(f) = O(R_{0}(f)^{2}) (folklore, I think)

R_{0}(f) = O(R^{2} log(n)) (Midrijanis)

D(f) = O(Q(f)^{6}) (Beals et al. 1998)

OK, so what were the largest known gaps? For D versus R_{0} (as well as D versus R), the largest known gap since 1986 has come from the “recursive AND/OR tree”: that is, an OR of two ANDs of two ORs of two ANDs of … forming a complete binary tree of depth d, with the n=2^{d} input variables comprising the leaves. For this function, we have D(f)=n, whereas Saks and Wigderson showed that R_{0}(f)=Θ(n^{0.753}) (and later, Santha showed that R(f)=Θ(n^{0.753}) as well).

For D versus Q, the largest gap has been for the OR function: we have D(OR)=n (as mentioned earlier), but Q(OR)=Θ(√n) because of Grover’s algorithm. Finally, for R_{0} versus R, *no* asymptotic gap has been known for any total function. (This is a problem that I clearly remember working on back in 2000, when I was an undergrad. I even wrote a computer program, the Boolean Function Wizard, partly to search for separations between R_{0} versus R. Alas, while I *did* find one or two functions with separations, I was unable to conclude anything from them about asymptotics.)

So, how did Ambainis et al. achieve bigger gaps for each of these? I’ll *try* to have an explanation written by the time my flight from Portland to Boston has landed tonight. But if you can’t wait for that, or you prefer it straight from the horse’s mouth, read their paper!

**Addendum 2: The Actual Functions**

As I mentioned before, the starting point for everything Ambainis et al. do is a certain Boolean function g recently constructed by Göös, Pitassi, and Watson (henceforth GPW), for different purposes than the ones that concern Ambainis et al. We think of the inputs to g as divided into nm “cells,” which are arranged in a rectangular grid with m columns and n rows. Each cell contains a bit that’s either 0 or 1 (its “label), as well as a pointer to another cell (consisting of ~log_{2}(nm) bits). The pointer can also be “null” (i.e., can point nowhere). We’ll imagine that a query of a cell gives you everything: the label *and* all the bits of the pointer. This could increase the query complexity of an algorithm, but only by a log(n) factor, which we won’t worry about.

Let X be a setting of all the labels and pointers in the grid. Then the question we ask about X is the following:

- Does there exist a “marked column”: that is, a column where all n of the labels are 1, and which has exactly one non-null pointer, which begins a chain of pointers of length m-1, which visits exactly one “0” cell in each column other than the marked column, and then terminates at a null pointer?

If such a marked column exists, then we set g(X)=1; otherwise we set g(X)=0. Crucially, notice that if a marked column exists, then it’s *unique*, since the chain of pointers “zeroes out” all m-1 of the other columns, and prevents them from being marked.

This g *already* leads to a new query complexity separation, one that refutes a strengthened form of the Saks-Wigderson conjecture. For it’s not hard to see that D(g)=Ω(mn): indeed, any deterministic algorithm must query almost all of the cells. A variant of this is proved in the paper, but the basic idea is that an adversary can answer all queries with giant fields of ‘1’ labels and null pointers—*until* a given column is almost completed, at which point the adversary fills in the last cell with a ‘0’ label and a pointer to the *last* ‘0’ cell that it filled in. The algorithm just can’t catch a break; it will need to fill in m-1 columns before it knows where the marked one is (if a marked column exists at all).

By contrast, it’s possible to show that, if n=m, then R(g) is about O(n^{4/3}). I had an argument for R(g)=O((n+m)log(m)) in an earlier version of this post, but the argument was wrong; I thank Alexander Belov for catching the error. I’ll post the R(g)=O(n^{4/3}) argument once I understand it.

To get the other separations—for example, total Boolean functions for which D~R_{0}^{2}, D~Q^{4}, R_{0}~Q^{3}, R_{0}~R^{3/2}, and R~approxdeg^{4}—Ambainis et al. need to add various “enhancements” to the basic GPW function g defined above. There are three enhancements, which can either be added individually or combined, depending on one’s needs.

1. Instead of just a single marked column, we can define g(X) to be 1 if and only if there are k marked columns, which point to each other in a cycle, and which *also* point to a trail of m-k ‘0’ cells, showing that none of the other columns contain all ‘1’ cells. This can help a bounded-error randomized algorithm—which can quickly find one of the all-1 columns using random sampling—while not much helping a zero-error randomized algorithm.

2. Instead of a linear *chain* of pointers showing that all the non-marked columns contain a ‘0’ cell, for g(X) to be 1 we can demand a complete *binary tree* of pointers, originating at a marked column and fanning out to all the unmarked columns in only log(m) layers. This can substantially help a quantum algorithm, which can’t follow a pointer trail any faster than a classical algorithm can; but which, given a complete binary tree, can “fan out” and run Grover’s algorithm on all the leaves in only the square root of the number of queries that would be needed classically. Meanwhile, however, putting the pointers in a tree doesn’t much help deterministic or randomized algorithms.

3. In addition to pointers “fanning out” from a marked column to all of the unmarked columns, we can demand that in every unmarked column, some ‘0’ cell contains a *back-pointer*, which leads back to a marked column. These back-pointers can help a randomized or quantum algorithm find a marked column faster, while not much helping a deterministic algorithm.

Unless I’m mistaken, the situation is this:

With no enhancements, you can get D~R^{2} and something like D~R_{0}^{3/2} (~~although I still don’t understand how you get the latter with no enhancements; the paper mentions it without proof~~ Andris has kindly supplied a proof here).

With only the cycle enhancement, you can get R_{0}~R^{3/2}.

With only the binary tree enhancement, you can get R~approxdeg^{4}.

With only the back-pointer enhancement, you can get D~R_{0}^{2}.

With the cycle enhancement *and* the binary-tree enhancement, you can get R_{0}~Q^{3}.

With the back-pointer enhancement *and* the binary-tree enhancement, you can get D~Q^{4}.

It’s an interesting question whether there are separations that require both the cycle enhancement and the back-pointer enhancement; Ambainis et al. don’t give any examples.

And here’s *another* interesting question not mentioned in the paper. Using the binary-tree enhancement, Ambainis et al. achieve a fourth-power separation between bounded-error randomized query complexity and approximate degree as a real polynomial—i.e., quadratically better than any separation that was known before. Their proof of this involves cleverly constructing a low-degree polynomial by *summing* a bunch of low-degree polynomials derived from quantum algorithms (one for each possible marked row). As a result, their final, summed polynomial does *not* itself correspond to a quantum algorithm, meaning that they don’t get a fourth-power separation between R and Q (which would’ve been even more spectacular than what they do get). On the other hand, purely from the existence of a function with R~approxdeg^{4}, we can deduce that that function has *either*

(i) a super-quadratic gap between R and Q (refuting my conjecture that the Grover speedup is the best possible quantum speedup for total Boolean functions), or

(ii) a quadratic gap between quantum query complexity and approximate degree—substantially improving over the gap found by Ambainis in 2003.

I conjecture that the truth is (ii); it would be great to have a proof or disproof of this.

]]>When the organizers asked me for a brief statement about why I signed, I sent them the following:

Signing this petition wasn’t an obvious choice for me, since I’m sensitive to the charge that divestment petitions are just meaningless sanctimony, a way for activists to feel morally pure without either making serious sacrifices or engaging the real complexities of an issue. In the end, though, that kind of meta-level judgment can’t absolve us of the need to consider each petition on its merits: if we think of a previous crisis for civilization (say, in the late 1930s), then it seems obvious that even symbolic divestment gestures were better than nothing. What made up my mind was reading the arguments pro and con, and seeing that the organizers of this petition had a clear-eyed understanding of what they were trying to accomplish and why: they know that divestment can’t directly drive down oil companies’ stock prices, but it can powerfully signal to the world a scientific consensus that, if global catastrophe is to be averted, most of the known fossil-fuel reserves need to be left in the ground, and that current valuations of oil, gas, and coal companies fail to reflect that reality.

For some recent prognoses of the climate situation, see (for example) this or this from *Vox*. My own sense is that the threat has been systematically *under*stated even by environmentalists, because of the human impulse to shoehorn all news into a hopeful narrative (“but there’s still time! if we just buy locally-grown produce, everything can be OK!”). Logically, there’s an obvious tension between the statements:

(a) there was already an urgent need to act decades ago, and

(b) having failed to act then, we can still feasibly avert a disaster now.

And indeed, (b) appears false to me. We’re probably well into the era where, regardless of what we do or don’t do, some of us *will* live to see a climate dramatically different from the one in which human civilization developed for the past 10,000 years, at least as different as the last Ice Ages were.

And yet that fact still doesn’t relieve us of moral responsibility. We can buy more time to prepare, hoping for technological advances in the interim; we can try to bend the curve of CO_{2} concentration away from the worst futures and toward the merely terrible ones. Alas, even those steps will require political will that’s unprecedented outside of major wars. For the capitalist free market (which I’m a big fan of) to work its magic, actual costs first need to get reflected in prices—which probably means massively taxing fossil fuels, to the point where it’s generally cheaper to leave them in the ground and switch to alternatives. (Lest anyone call me a doctrinaire treehugger, I also support *way* less regulation of the nuclear industry, to drive down the cost of building the hundreds of new nuclear plants that we’ll probably need.)

These realities have a counterintuitive practical implication that I wish both sides understood better. Namely, if you share my desperation and terror about this crisis, the urgent desire to do something, then *limiting your personal carbon footprint should be very far from your main concern*. Like, it’s great if you can bike to work, and you should keep it up (fresh air and exercise and all). But I’d say the anti-environmentalists are *right* that such voluntary steps are luxuries of the privileged, and will accordingly never add up to a hill of beans. Let me go further: even to *conceptualize* this problem in terms of personal virtue and blame seems to me like a tragic mistake, one on which the environmentalists and their opponents colluded. Given the choice, I’d much rather that the readers of this blog flew to all the faraway conferences they wanted, drove gas-guzzling minivans, ate steaks every night, and had ten kids, but then *also* took some steps that made serious political action to leave most remaining fossil fuels in the ground even ε more likely, ε closer to the middle of our Overton window. I signed the MIT divestment petition because it seemed to me like such a step, admittedly with an emphasis on the ε.

As he puts it:

This probably sounds silly, but I’ve been existentially troubled by certain science fiction predictions for about a year or two, most of them coming from the Ray Kurzweil/Singularity Institute types … What really bothers me is the idea of the “abolition of suffering” as some put it. I just don’t see the point. Getting rid of cancer, premature death, etc., that all sounds great. But death itself? All suffering? At what point do we just sit down and ask ourselves, why not put our brains in a jar, and just activate our pleasure receptors for all eternity? That seems to be the logical conclusion of that line of thinking. If we want to reduce the conscious feeling of pleasure to the release of dopamine in the brain, well, why not?

I guess what I think I’m worried about is having to make the choice to become a cyborg, or to upload my mind to a computer, to live forever, or to never suffer again. I don’t know how I’d answer, given the choice. I enjoy being human, and that includes my suffering. I really don’t want to live forever. I see that as a hedonic treadmill more than anything else. Crazy bioethicists like David Pearce, who want to genetically re-engineer all species on planet Earth to be herbivores, and literally abolish all suffering, just add fuel to my anxiety.

… Do you think we’re any closer to what Kurzweil (or Pearce) predicted (and by that I mean, will we see it in our lifetimes)? I want to stop worrying about these things, but something is preventing me from doing so. Thoughts about the far flung (or near) future are just intrusive for me. And it seems like everywhere I go I’m reminded of my impending fate. Ernst Jünger would encourage me to take up an attitude of amor fati, but I can’t see myself doing that. My father says I’m too young to worry about these things, and that the answer will be clear when I’ve actually lived my life. But I just don’t know. I want to stop caring, more than anything else. It’s gotten to a point where the thoughts keep me up at night.

I don’t know how many readers might have had similar anxieties, but in any case, I thought my reply might be of some interest to others, so with the questioner’s kind permission, I’m reproducing it below.

1. An end to suffering removing the meaning from life? As my grandmother might say, “we should only have such problems”! I believe, alas, that suffering will always be with us, even after a hypothetical technological singularity, because of basic Malthusian logic. I.e., no matter how many resources there are, population will expand exponentially to exploit them and make the resources scarce again, thereby causing fighting, deprivation, and suffering. What’s terrifying about Malthus’s logic is how fully general it is: it applies equally to tenure-track faculty positions, to any extraterrestrial life that might exist in our universe or in any other bounded universe, and to the distant post-Singularity future.

But if, by some miracle, we were able to overcome Malthus and eliminate all suffering, my own inclination would be to say “go for it”! I can easily imagine a life that was well worth living—filled with beauty, humor, play, love, sex, and mathematical and scientific discovery—even though it was devoid of any serious suffering. (We could debate whether the “ideal life” would include occasional setbacks, frustrations, etc., even while agreeing that at any rate, it should certainly be devoid of cancer, poverty, bullying, suicidal depression, and one’s Internet connection going down.)

2. If you want to worry about something, then rather than an end to suffering, I might humbly suggest worrying about a large *increase* in human suffering within our lifetimes. A few possible culprits: climate change, resurgent religious fundamentalism, large parts of the world running out of fresh water.

3. It’s fun to think about these questions from time to time, to use them to hone our moral intuitions—and I even agree with Scott Alexander that it’s worthwhile to have a small number of smart people think about them full-time for a living. But I should tell you that, as I wrote in my post The Singularity Is Far, I don’t expect a Singularity in my lifetime or my grandchildrens’ lifetimes. Yes, technically, if there’s ever going to be a Singularity, then we’re 10 years closer to it now than we were 10 years ago, but it could still be one hell of a long way away! And yes, I expect that technology will continue to change in my lifetime in amazing ways—not as much as it changed in my grandparents’ lifetimes, probably, but still by a lot—but how to put this? I’m willing to bet any amount of money that when I die, people’s shit will still stink.

]]>So, what was (or is) inside the NSA’s cryptanalytic black box? A quantum computer? Maybe even one that they bought from D-Wave? (Rimshot.) A fast classical factoring algorithm? A proof of P=NP? Commentators on the Internet rushed to suggest each of these far-reaching possibilities. Some of us tried to pour cold water on these speculations—pointing out that one could envision many scenarios that were a little more prosaic, a little more tied to the details of how public-key crypto is actually used in the real world. Were we just naïve?

This week, a new bombshell 14-author paper (see also the website) advances an exceedingly plausible hypothesis about what *may* have been the NSA’s greatest cryptanalytic secret of recent years. One of the authors is J. Alex Halderman of the University of Michigan, my best friend since junior high school, who I’ve blogged about before. Because of that, I had some advance knowledge of this scoop, and found myself having to do what regular *Shtetl-Optimized* readers will know is the single hardest thing in the world for me: *bite my tongue and not say anything.* Until now, that is.

Besides Alex, the other authors are David Adrian, Karthikeyan Bhargavan, Zakir Durumeric, Pierrick Gaudry, Matthew Green, Nadia Heninger, Drew Springall, Emmanuel Thomé, Luke Valenta, Benjamin VanderSloot, Eric Wustrow, Santiago Zanella-Béguelink, and Paul Zimmermann (two of these, Green and Heninger, have previously turned up on *Shtetl-Optimized*).

These authors study vulnerabilities in Diffie-Hellman key exchange, the “original” (but still widely-used) public-key cryptosystem, the one that predates even RSA. Diffie-Hellman is the thing where Alice and Bob first agree on a huge prime number *p* and a number *g*, then Alice picks a secret *a* and sends Bob *g ^{a}* (mod

As far as anyone knows today, the best way to break Diffie-Hellman is simply by calculating discrete logarithms: that is, solving the problem of recovering *a* given only *g* and *h*=*g ^{a}* (mod

(Note that the recent breakthrough of Antoine Joux, solving discrete log in quasipolynomial time in fields of small characteristic, also relied heavily on sieving ideas. But there are no improvements from this yet for the “original” discrete log problem, over prime fields.)

But there’s one crucial further fact, which has been understood for at least a decade by theoretical cryptographers, but somehow was slow to filter out to the people who deploy practical cryptosystems. The further fact is that in NFS, *you can arrange things so that almost all the discrete-logging effort depends only on the prime number p, and not at all on the specific numbers g and h for which you’re trying to take the discrete log*. After this initial “precomputation” step, you then have a massive database that you can use to speed up the “descent” step: the step of solving *g ^{a}*=

It’s a little like the complexity class P/poly, where a single, hard-to-compute “advice string” unlocks exponentially many inputs once you have it. (Or a bit more precisely, one could say that NFS reveals that exponentiation modulo a prime number is *sort of* a trapdoor one-way function, except that the trapdoor information is subexponential-size, and given the trapdoor, inverting the function is still subexponential-time, but a milder subexponential than before.)

The kicker is that, in practice, a large percentage of all clients and servers that use Diffie-Hellman key exchange use the same few prime numbers *p*. This means that, if you wanted to decrypt a large fraction of all the traffic encrypted with Diffie-Hellman, you wouldn’t need to do NFS over and over: you could just do it for a few *p*‘s and cache the results. This fact can singlehandedly change the outlook for breaking Diffie-Hellman.

The story is different depending on the key size, log(*p*). In the 1990s, the US government insisted on “export-grade” cryptography for products sold overseas (what a quaint concept!), which meant that the key size was restricted to 512 bits. For 512-bit keys, Adrian et al. were able to implement NFS and use it to do the precomputation step in about 7 days on a cluster with a few thousand cores. After this initial precomputation step (which produced 2.5GB of data), doing the descent, to find the discrete log for a specific (*g*,*h*) pair, took only about 90 seconds on a 24-core machine.

OK, but no one *still* uses 512-bit keys, do they? The first part of Adrian et al.’s paper demonstrates that, because of implementation issues, even today you can force many servers to “downgrade” to the 512-bit, export-grade keys—and then, having done so, you can stall for time for 90 seconds as you figure out the session key, and then do a man-in-the-middle attack and take over and impersonate the server. It’s an impressive example of the sort of game computer security researchers have been playing for a long time—but it’s really just a warmup to the main act.

As you’d expect, many servers today are configured more intelligently, and will only agree to 1024-bit keys. But even there, Adrian et al. found that a large fraction of servers rely on just a single 1024-bit prime (!), and many of the ones that don’t rely on just a few other primes. Adrian et al. estimate that, for a single 1024-bit prime, doing the NFS precomputation would take about 45 million years using a single core—or to put it more ominously, 1 year using 45 million cores. If you built special-purpose hardware, that could go down by almost two orders of magnitude, putting the monetary cost at a few hundred million dollars, completely within the reach of a sufficiently determined nation-state. Once the precomputation was done, and the terabytes of output stored in a data center somewhere, computing a particular discrete log would then take about 30 days using 1 core, or mere minutes using a supercomputer. Once again, none of this is assuming any algorithmic advances beyond what’s publicly known. (Of course, it’s possible that the NSA *also* has some algorithmic advances; even modest ones could obviate the need for special-purpose hardware.)

While writing this post, I did my own back-of-the-envelope, and got that using NFS, calculating a 1024-bit discrete log should be about 7.5 million times harder than calculating a 512-bit discrete log. So, extrapolating from the 7 days it took Adrian et al. to do it for 512 bits, this suggests that it might’ve taken them about 143,840 years to calculate 1024-bit discrete logs with the few thousand cores they had, or 1 year if they had 143,840 times as many cores (since almost all this stuff is extremely parallelizable). Adrian et al. mention optimizations that they expect would improve this by a factor of 3, giving us about 100 million core-years, very similar to Adrian et al.’s estimate of 45 million core-years (the lower-order terms in the running time of NFS might account for some of the remaining discrepancy).

Adrian et al. mount a detailed argument in their paper that all of the details about NSA’s “groundbreaking cryptanalytic capabilities” that we learned from the Snowden documents match what *would* be true if the NSA were doing something like the above. The way Alex put it to me is that, sure, the NSA might not have been doing this, but if not, then he would like to understand *why* not—for it would’ve been completely feasible within the cryptanalytic budget they had, and the NSA would’ve known that, and it would’ve been a very good codebreaking value for the money.

Now that we know about this weakness of Diffie-Hellman key exchange, what can be done?

The most obvious solution—but a good one!—is just to use longer keys. For decades, when applied cryptographers would announce some attack like this, theorists like me would say with exasperation: “dude, why don’t you fix *all* these problems in one stroke by just, like, increasing the key sizes by a factor of 10? when it’s an exponential against a polynomial, we all know the exponential will win eventually, so why not just go out to where it does?” The applied cryptographers explain to us, with equal exasperation in their voices, that there are all sorts of reasons why not, from efficiency to (maybe the biggest thing) *backwards-compatibility.* You can’t unilaterally demand 2048-bit keys, if millions of your customers are using browsers that only understand 1024-bit keys. On the other hand, given the new revelations, it looks like there really will be a big push to migrate to larger key sizes, as the theorists would’ve suggested from their ivory towers.

A second, equally-obvious solution is to *stop relying so much on the same few prime numbers* in Diffie-Hellman key exchange. (Note that the reason RSA isn’t vulnerable to this particular attack is that it inherently requires a different composite number *N* for each public key.) In practice, generating a new huge random prime number tends to be expensive—taking, say, a few minutes—which is why people so often rely on “standard” primes. At the least, we could use libraries of millions of “safe” primes, from which a prime for a given session is chosen randomly.

A third solution is to migrate to elliptic-curve cryptography (ECC), which as far as anyone knows today, is much less vulnerable to descent attacks than the original Diffie-Hellman scheme. Alas, there’s been a lot of understandable distrust of ECC after the DUAL_EC_DBRG scandal, in which it came out that the NSA backdoored some of NIST’s elliptic-curve-based pseudorandom generators by choosing particular parameters that it knew how handle. But maybe the right lesson to draw is mod-p groups and elliptic-curve groups *both* seem to be pretty good for cryptography, but the mod-p groups are less good if everyone is using the same few prime numbers p (*and* those primes are “within nation-state range”), and the elliptic-curve groups are less good if everyone is using the same few parameters. (A lot of these things do seem pretty predictable with hindsight, but how many did *you* predict?)

Many people will use this paper to ask political questions, like: hasn’t the NSA’s codebreaking mission once again usurped its mission to ensure the nation’s information security? Doesn’t the 512-bit vulnerability that many Diffie-Hellman implementations still face, as a holdover from the 1990s export rules, illustrate why encryption should never be deliberately weakened for purposes of “national security”? How can we get over the issue of backwards-compatibility, and get everyone using strong crypto? People absolutely should be asking such questions.

But for readers of this blog, there’s one question that probably looms even larger than those of freedom versus security, openness versus secrecy, etc.: namely, the question of theory versus practice. Which “side” should be said to have “won” this round? Some will say: those useless theoretical cryptographers, they didn’t even know how their coveted Diffie-Hellman system could be broken in the real world! The theoretical cryptographers might reply: *of course* we knew about the ability to do precomputation with NFS! This wasn’t some NSA secret; it’s something we discussed openly for years. And if someone told us how Diffie-Hellman was actually being used (with much of the world relying on the same few primes), we could’ve immediately spotted the potential for such an attack. To which others might reply: then why didn’t you spot it?

Perhaps the right lesson to draw is how silly such debates really are. In the end, piecing this story together took a team that was willing to do everything from learning some fairly difficult number theory to coding up simulations to poring over the Snowden documents for clues about the NSA’s budget. Clear thought doesn’t respect the boundaries between disciplines, or between theory and practice.

*(Thanks very much to Nadia Heninger and Neal Koblitz for reading this post and correcting a few errors in it. For more about this, see Bruce Schneier’s post or Matt Green’s post.)*

2. Back in January, I blogged about a new quantum optimization algorithm by Farhi, Goldstone, and Gutmann, which was notable for being, as far as anyone could tell, the first quantum algorithm to achieve a provably better approximation ratio than the best-known classical algorithm for an NP-hard optimization problem. Today, I report that a fearsome list of authors—Boaz Barak, Ankur Moitra, Ryan O’Donnell, Prasad Raghavendra, Oded Regev, David Steurer, Luca Trevisan, Aravindan Vijayaraghavan, David Witmer, and John Wright—has put out an eagerly-awaited paper that gives a classical algorithm for the same problem, with better performance than the quantum algorithm’s. (They write that this “improves both qualitatively and quantitatively” on Farhi et al.’s work; I assume “qualitatively” refers to the fact that the new algorithm is classical.) What happened, apparently, is that after I blogged (with enthusiasm) about the Farhi et al. result, a bunch of classical complexity theorists read my post and decided independently that they could match or beat the quantum algorithm’s performance classically; then they found out about each other and decided to merge their efforts. I’m proud to say that this isn’t the first example of this blog catalyzing actual research progress, though it’s probably the best example so far. [Update: Luca Trevisan now has a great post explaining what happened in much more detail, entitled “How Many Theoreticians Does It Take to Approximate Max 3Lin?”]

3. Jennifer Ouellette has a wonderful article in *Quanta* magazine about recent progress in AdS/MERA (i.e., “the emergence of spacetime from entanglement”), centered around the ideas of Brian Swingle. This is one of the main things that I’d love to understand better right now—if I succeed even partially, you’ll know because I’ll write a blog post trying to explain it to others. See also this blog post by Sean Carroll (about this paper by Ning Bao et al.), and this paper by Pastawski, Yoshida, Harlow, and Preskill, which explicitly mines the AdS/CFT correspondence for examples of quantum error-correcting codes.

4. Celebrity rationalist Julia Galef, who I had the great honor of meeting recently, has a podcast interview with Sean Carroll about why Carroll accepts the many-worlds interpretation. (Or if, like me, you prefer the written word to the spoken one, click here for a full transcript.) Unfortunately, Sean is given the opportunity at the end of the interview to recommend one science book to his listeners—just one!—but he squanders it by plugging some weird, self-indulgent thing called *Quantum Computing Since Democritus*. Julia also has a YouTube video about what she learned from the interview, but I haven’t yet watched it (is there a transcript?).

5. I came across an insightful if meandering essay about nerd culture by Meredith L. Patterson. In particular, noticing how the term “nerd” has been co-opted by normal, socially-skilled people, who’ve quickly set about remaking nerd social norms to make them identical to the rest of the world’s norms, Patterson coins the term “weird-nerd” to describe people like herself, who are still nerds *in the original sense* and who don’t see nerd culture as something horribly, irreparably broken. As she writes: “We’ll start to feel less defensive when we get some indication — any indication — that our critics understand what parts of our culture we don’t want to lose and why we don’t want to lose them.” (But is this the start of a linguistic treadmill? Will we eventually need to talk about *weird*-weird-nerds, etc.?)