Quantum

The Unitary Fund Expands Into Its Next Phase with Additional Support

dougfinke1 — Thu, 21 Nov 2019 17:01:58 +0000

The Unitary Fund was established in 2018 to provide small micro-grants of $2,000 to fund people who wanted to explore open source quantum software projects. Since then it has funded eleven different projects in ten countries resulting in several technical papers being published and has helped to launch one new quantum startup.

They have just posted a new article on Medium in which the Unitary Fund announces that it is entering a new phase with funding from IBM, Google X, Microsoft, Rigetti, Xanadu, and Zapata Computing. The new funding will allow the standard micro-grant size to double to $4,000 and allow the Unitary Fund to support up to 40 new projects. In addition, the expanded program may allow funding of a broader range of projects and potentially include projects related to quantum hardware, quantum sensors, and other things that are not within the realm of open source quantum software.

Also in the announcement, the Unitary Fund announced that it has formed an internal research program which they call Unitary Labs. This effort will perform quantum research along with partners from universities and government labs. One such project is the TEAM (Tough Errors are No Match) project being led by the University of Maryland which aims to improve the toolkit available for quantum compilers so that quantum hardware can be more robust to noisy components.

For more you can view the Medium article announcing the expanded Unitary Fund here and to see more details about their funding process and to apply for one of the micro-grants you can visit https://unitary.fund/.

A super-fast 'light switch' for future cars and computers

Wed, 20 Nov 2019 18:13:27 +0000

Switching light beams quickly is important in many technological applications. Researchers have now developed an 'electro-opto-mechanical' switch for light beams that is considerably smaller and faster than current models. This is relevant for applications such as self-driving cars and optical quantum technologies.

CQC, Xanadu Release New Software Versions for Improved Multi-Platform Support

dougfinke1 — Tue, 19 Nov 2019 22:47:49 +0000

Cambridge Quantum Computing (CQC) has released version 0.4 of pytket, a Python tool for interfacing to their t|ket> quantum programming tools. Key features of this version include improvements in circuit optimization and qubit routing as well as a consistent back-end interface to allow easy switch between different quantum computing platforms or simulators. Currently, t|ket> support the Google Cirq, IBM Qiskit, Pyzx, ProjectQ and Rigetti pyQuil platforms. One of the strong points of t|ket> is its high performance circuit optimizer which can take advantage of device calibration data, when available, to achieve the best fidelity results for a given processor. For more details about pytket 0.4, you can view their news release here.

Xanadu has released a Cirq plug-in for Pennylane, a Python library for quantum machine learning, automatic differentiation, and optimization of hybrid quantum-classical computations. Pennylane also provides multi-platform support for other platforms including their own Strawberry Fields, IBM Q, Google Cirq, Rigetti Forest, Microsoft QDK, and ProjectQ. For more information on Xanadu’s Cirq plug-in you can click here to go to their GitHub site for the Pennylane-Cirq plug-in module.

November 19, 2019

Artificial intelligence algorithm can learn the laws of quantum mechanics

Tue, 19 Nov 2019 15:54:57 +0000

Artificial intelligence can be used to predict molecular wave functions and the electronic properties of molecules. This innovative AI method could be used to speed-up the design of drug molecules or new materials.

Quantum light improves sensitivity of biological measurements

Mon, 18 Nov 2019 20:24:11 +0000

In a new study, researchers showed that quantum light can be used to track enzyme reactions in real time. The work brings together quantum physics and biology in an important step toward the development of quantum sensors for biomedical applications.

Kick-starting Moore's Law? New 'synthetic' method for making microchips could help

Mon, 18 Nov 2019 19:03:32 +0000

Researchers have developed a new method for producing atomically-thin semiconducting crystals that could one day enable more powerful and compact electronic devices.

Quantum computers learn to mark their own work

Mon, 18 Nov 2019 19:03:23 +0000

A new test to check if a quantum computer is giving correct answers to questions beyond the scope of traditional computing could help the first quantum computer that can outperform a classical computer to be realized.

Will quantum computers revolutionize the world?

leah — Mon, 18 Nov 2019 15:00:13 +0000

From the College of DuPage's The Courier, Nov. 14, 2019: Fermilab scientists Jim Kowalski and Stephen Mrenna talk about Fermilab is using quantum computing to solve the problems of the universe.

The Aaronson-Ambainis Conjecture (2008-2019)

Scott — Sun, 17 Nov 2019 23:33:08 +0000

Around 1999, one of the first things I ever did in quantum computing theory was to work on a problem that Lance Fortnow suggested in one of his papers: is it possible to separate P from BQP relative to a random oracle? (That is, without first needing to separate P from PSPACE or whatever in the real world?) Or to the contrary: suppose that a quantum algorithm Q makes T queries to a Boolean input string X. Is there then a classical simulation algorithm that makes poly(T) queries to X, and that approximates Q’s acceptance probability for most values of X? Such a classical simulation, were it possible, would still be consistent with the existence of quantum algorithms like Simon’s and Shor’s, which are able to achieve exponential (and even greater) speedups in the black-box setting. It would simply underscore the importance, for Simon’s and Shor’s algorithms, of global structure that makes the string X extremely non-random: for example, encoding a periodic function (in the case of Shor’s algorithm), or encoding a function that hides a secret string s (in the case of Simon’s). It would underscore that superpolynomial quantum speedups depend on structure.

I never managed to solve this problem. Around 2008, though, I noticed that a solution would follow from a perhaps-not-obviously-related conjecture, about influences in low-degree polynomials. Namely, let p:Rⁿ→R be a degree-d real polynomial in n variables, and suppose p(x)∈[0,1] for all x∈{0,1}ⁿ. Define the variance of p to be Var(p):=E_x,y[|p(x)-p(y)|], and define the influence of the i^th variable to be Inf_i(p):=E_x[|p(x)-p(xⁱ)|]. Here the expectations are over strings in {0,1}ⁿ, and xⁱ means x with its i^th bit flipped between 0 and 1. Then the conjecture is this: there must be some variable i such that Inf_i(p) > poly(Var(p)/d) (in other words, that “explains” a non-negligible fraction of the variance of the entire polynomial).

Why would this conjecture imply the statement about quantum algorithms? Basically, because of the seminal result of Beals et al. from 1998: that if a quantum algorithm makes T queries to a Boolean input X, then its acceptance probability can be written as a real polynomial over the bits of X, of degree at most 2T. Given that result, if you wanted to classically simulate a quantum algorithm Q on most inputs—and if you only cared about query complexity, not computation time—you’d simply need to do the following:
(1) Find the polynomial p that represents Q’s acceptance probability.
(2) Find a variable i that explains at least a 1/poly(T) fraction of the total remaining variance in p, and query that i.
(3) Keep repeating step (2), until p has been restricted to a polynomial with not much variance left—i.e., to nearly a constant function p(X)=c. Whenever that happens, halt and output the constant c.
The key is that by hypothesis, this algorithm will halt, with high probability over X, after only poly(T) steps.

Anyway, around the same time, Andris Ambainis had a major break on a different problem that I’d told him about: namely, whether randomized and quantum query complexities are polynomially related for all partial functions with permutation symmetry (like the collision and the element distinctness functions). Andris and I decided to write up the two directions jointly. The result was our 2011 paper entitled The Need for Structure in Quantum Speedups.

Of the two contributions in the “Need for Structure” paper, the one about random oracles and influences in low-degree polynomials was clearly the weaker and less satisfying one. As the reviewers pointed out, that part of the paper didn’t solve anything: it just reduced one unsolved problem to a new, slightly different problem that was also unsolved. Nevertheless, that part of the paper acquired a life of its own over the last decade, as the world’s experts in analysis of Boolean functions and polynomials began referring to the “Aaronson-Ambainis Conjecture.” Ryan O’Donnell, Guy Kindler, and many others had a stab. I even got Terry Tao to spend an hour to two on the problem when I visited UCLA.

Now, at long last, Nathan Keller and Ohad Klein say they’ve solved the problem, in a preprint whose title is a riff on ours: “Quantum Speedups Need Structure.”

Their paper hasn’t yet been peer-reviewed, and I haven’t yet carefully studied it, but I could and should: at 19 pages, it looks very approachable and clear, if not as radically short as (say) Huang’s proof of the Sensitivity Conjecture. Keller and Klein’s argument subsumes all the earlier results that I knew would need to be subsumed, and involves all the concepts (like a real analogue of block sensitivity) that I knew would need to be involved.

My plan had been as follows:
(1) Read their paper in detail. Understand every step of their proof.
(2) Write a blog post that reflects my detailed understanding.

Unfortunately, this plan did not sufficiently grapple with the fact that I now have two kids. It got snagged for a week at step (1). So I’m now executing an alternative plan, which is to jump immediately to the blog post.

Anyway, assuming Keller and Klein’s result holds up—as I expect it will—by combining it with the observations in my and Andris’s paper, one immediately gets an explanation for why no one has managed to separate P from BQP relative to a random oracle (but only relative to non-random oracles). This complements the work of Kahn, Saks, and Smyth, who around 2000 gave a precisely analogous explanation for the difficulty of separating P from NP∩coNP relative to a random oracle. Unfortunately, the polynomial blowup is quite enormous: from a quantum algorithm making T queries, Keller and Klein apparently get a classical algorithm making O(T¹⁸) queries. But such things can almost always be massively improved.

Feel free to use the comments to ask any questions about this result or its broader context. I’ll either do my best to answer from the limited amount I know, or else I’ll pass the questions along to Nathan and Ohad themselves. Maybe, at some point, I’ll even be forced to understand the new proof.

Congratulations to Nathan and Ohad!

Physicists irreversibly split photons by freezing them in Bose-Einstein condensate

Thu, 14 Nov 2019 19:12:46 +0000

Light can be directed in different directions, usually also back the same way. Physicists have however succeeded in creating a new one-way street for light. They cool photons down to a Bose-Einstein condensate, which causes the light to collect in optical 'valleys' from which it can no longer return. The findings could also be of interest for the quantum communication of the future.

Annual recruitment post

Scott — Tue, 12 Nov 2019 07:02:25 +0000

Just like I did last year, and the year before, I’m putting up a post to let y’all know about opportunities in our growing Quantum Information Center at UT Austin.

I’m proud to report that we’re building something pretty good here. This fall Shyam Shankar joined our Electrical and Computer Engineering (ECE) faculty to do experimental superconducting qubits, while (as I blogged in the summer) the quantum complexity theorist John Wright will join me on the CS faculty in Fall 2020. Meanwhile, Drew Potter, an expert on topological qubits, rejoined our physics faculty after a brief leave. Our weekly quantum information group meeting now regularly attracts around 30 participants—from the turnout, you wouldn’t know it’s not MIT or Caltech or Waterloo. My own group now has five postdocs and six PhD students—as well as some amazing undergrads striving to meet the bar set by Ewin Tang. Course offerings in quantum information currently include Brian La Cour’s Freshman Research Initiative, my own undergrad Intro to Quantum Information Science honors class, and graduate classes on quantum complexity theory, experimental realizations of QC, and topological matter (with more to come). We’ll also be starting an undergraduate Quantum Information Science concentration next fall.

So without further ado:

(1) If you’re interested in pursuing a PhD focused on quantum computing and information (and/or classical theoretical computer science) at UT Austin: please apply! If you want to work with me or John Wright on quantum algorithms and complexity, apply to CS (I can also supervise physics students in rare cases). Also apply to CS, of course, if you want to work with our other CS theory faculty: David Zuckerman, Dana Moshkovitz, Adam Klivans, Anna Gal, Eric Price, Brent Waters, Vijaya Ramachandran, or Greg Plaxton. If you want to work with Drew Potter on nonabelian anyons or suchlike, or with Allan MacDonald, Linda Reichl, Elaine Li, or others on many-body quantum theory, apply to physics. If you want to work with Shyam Shankar on superconducting qubits, apply to ECE. Note that the deadline for CS and physics is December 1, while the deadline for ECE is December 15.

You don’t need to ask me whether I’m on the lookout for great students: I always am! If you say on your application that you want to work with me, I’ll be sure to see it. Emailing individual faculty members is not how it works and won’t help. Admissions are extremely competitive, so I strongly encourage you to apply broadly to maximize your options.

(2) If you’re interested in a postdoc in my group, I’ll have approximately two openings starting in Fall 2020. To apply, just send me an email by January 1, 2020 with the following info:
– Your CV
– 2 or 3 of your best papers (links or PDF attachments)
– The names of two recommenders (who should email me their letters separately)

(3) If you’re on the faculty job market in quantum computing and information—well, please give me a heads-up if you’re potentially interested in Austin! Our CS, physics, and ECE departments are all open to considering additional candidates in quantum information, both junior and senior. I can’t take credit for this—it surely has to do with developments beyond my control, both at UT and beyond—but I’m happy to relay that, in the three years since I arrived in Texas, the appetite for strengthening UT’s presence in quantum information has undergone jaw-dropping growth at every level of the university.

Also, Austin-Bergstrom International Airport now has direct flights to London, Frankfurt, and (soon) Amsterdam.

Hook ’em Hadamards!

Microsoft Announces Azure Quantum with Partners IonQ, Honeywell, QCI, and 1QBit

dougfinke1 — Sat, 09 Nov 2019 01:58:31 +0000

Microsoft has announced its intent to offer quantum computing cloud services through its Azure cloud computing platform. The near term hardware platforms that they will support includes IonQ, Honeywell, and Quantum Circuits Inc. (QCI). IonQ and Honeywell are using the trapped ion technology while QCI is using a superconducting technology. Microsoft is indicating that they will eventually offer a hardware platform based upon their own topological based technology, but this technology is still in development and not available in the near term.

Microsoft Quantum Stack

It appears to us that this represents a relatively recent change in strategy. Up until a short time ago, Microsoft had indicated it was putting all its efforts behind its own topological approach which they believe will provide orders of magnitude better qubit quality than any of the other technology approaches. If they can make it a reality, it has the potential to become the technology of choice for quantum computing and make the others obsolete. But it is also a risky path as this technology is based upon a new type of particle called the Majorana fermion which has only been recently demonstrated.

In our view, it is a smart move by Microsoft to work with these other partners. Not only does this provide them with a backup plan in case the topological approach fails, but it also provide them with a means to start working with customers on real quantum hardware. Although Microsoft has been offering their Quantum Development Kit (QDK), Q# language and simulators for some time, we feel that having feedback from customers using actual quantum hardware may provide them with valuable insights and learning that will allow them to improve their own products.

The details of the Azure Quantum implementation are still sketchy. Customers will be able to program the quantum computers using Microsoft’s Q# language, but each of the different architectures will require a new and different backend to fit the specific machines from IonQ, Honeywell, and QCI. Also, details of the hardware platforms from these vendors has not yet been released. This includes qubit count, connectivity, native gates, coherence times, gate fidelities, etc., but we do expect additional announcements from these vendors with some of this information in the near future.

A final question for an end user will be which of the hardware platforms should they target for their application. This is an interesting question and answer may be a little complex. It will be interesting if Microsoft or anyone else provides end users with guidance on which of the backends is best for which application.

Nonetheless, users choices will become greatly expanded in 2020 and the additional competition will be to the end user’s advantage as they work to develop quantum computing applications that will provide their companies with commercial benefit.

For more information on this Azure Quantum partnership you can view Microsoft’s blog article, and associated news releases from IonQ, Honeywell, QCI, and 1QBit. And if you want to apply for preview access to become a early adopter, you can apply on Microsoft’s Azure Quantum web page here.

November 7, 2019

A distinct spin on atomic transport

Fri, 08 Nov 2019 20:54:38 +0000

Physicists demonstrate simultaneous control over transport and spin properties of cold atoms, and thus establish a framework for exploring concepts in spintronics and solid-state physics.

The morality of quantum computing

Scott — Thu, 07 Nov 2019 21:02:25 +0000

This morning a humanities teacher named Richard Horan, having read my NYT op-ed on quantum supremacy, emailed me the following question about it:

Is this pursuit [of scalable quantum computation] just an arms race? A race to see who can achieve it first? To what end? Will this achievement yield advances in medical science and human quality of life, or will it threaten us even more than we are threatened presently by our technologies? You seem rather sanguine about its possible development and uses. But how close does the hand on that doomsday clock move to midnight once we “can harness an exponential number of amplitudes for computation”?

I thought this question might possibly be of some broader interest, so here’s my response (with some light edits).

Dear Richard,

A radio interviewer asked me a similar question a couple weeks ago—whether there’s an ethical dimension to quantum computing research. I replied that there’s an ethical dimension to everything that humans do.

A quantum computer is not like a nuclear weapon: it’s not going to directly kill anybody (unless the dilution refrigerator falls on them or something?). It’s true that a full, scalable QC, if and when it’s achieved, will give a temporary advantage to people who want to break certain cryptographic codes. The morality of that, of course, could strongly depend on whether the codebreakers are working for the “good guys” (like the British breaking the Nazi codes during WWII) or the “bad guys” (like, perhaps, Trump or Vladimir Putin or Xi Jinping).

But in any case, there’s already a push to switch to new cryptographic codes that already exist and that we think are quantum-resistant. An actual scalable QC on the horizon would of course massively accelerate that push. And once people make the switch, we expect that security on the Internet will be more-or-less back where it started.

Meanwhile, the big upside potential from QCs is that they’ll provide an unprecedented ability to simulate physics and chemistry at the molecular level. That could at least potentially help with designing new medicines, as well as new batteries and solar cells and carbon capture technologies—all things that the world desperately needs right now.

Also, the theory developed around QC has already led to many new and profound insights about physics and computation. Some of us regard that as an inherent good, in the same way that art and music and literature are.

Now, one could argue that the climate crisis, or various other crises that our civilization faces, are so desperate that instead of working to build QCs, we should all just abandon our normal work and directly confront the crises, as (for example) Greta Thunberg is doing. On some days I share that position. But of course, were the position upheld, it would have implications not just for quantum computing researchers but for almost everyone on earth—including humanities teachers like yourself.

Best,
Scott

Why NISQ-MP May Become an Important Use Case for the Quantum Internet

dougfinke1 — Thu, 07 Nov 2019 18:50:13 +0000

It has long been our thesis that one can gain some good insights into how quantum computing technology will develop by reviewing what happened in the classical computing industry and envisioning whether similar things could happen with quantum computing. One of the areas where we see potential similarities is in the use of multi-processing technology.

In the initial stages of classical computing, processor systems consisted of just one processor. In order to achieve higher and higher performance, engineers found ways to increase it speed through the use of faster transistors, pipelining internal stages, and adding dedicated hardware to perform functionality previously implemented in software.

But at some point, the market continued to demand more and more performance and the computer designers ran out of tricks to provide computing capability with just a single processor that had the performance levels needed by their users. So computer designers turned to multi-processing to provide the increasing levels of computing capability. By having two or more processors working side-by-side, the amount of work that can be processed can be increased without requiring too much additional processor engineering work. But there are some drawbacks to this approach too. Software programs needed to be re-written to take advantage of the new parallel architectures. No longer could programmers view a problem as a linear step-by-step approach to writing their algorithms. They needed to figure out ways of parallelizing their algorithms without running into data access conflicts. Fast communication interfaces between the multiple processors needed to be developed to reduce potential interprocessor communication bottlenecks. Even so, there can be limits to how effective this technique can be because the performance increase available becomes less and less with each subsequent processor addition.

But slowly, the use of multiprocessor architectures became more and more prevalent. Initially, this occurred at the CPU design level, but not at the chip level. More recently, multi-core microprocessors became popular as the number of transistors available to be fit on one chip has continued to increase. It is now common to have data centers with thousands of processors in operation that can work together to accomplish amazing tasks such as providing a response to a search term inquiry that encompasses the entire internet within milliseconds.

It is quite possible that quantum computers will go down a similar multi-processor path. Designing a quantum machine not only has a many theoretical and electrical engineering challenges, but mechanical engineering challenges as well. As the number of qubits in a machine is increased, the number of control cables needs to increase as well. Not only does the space to fit them become a challenge, but more cables that need to be routed from room temperature control electronics to the millikelvin temperatures inside the dilution refrigerator can present additional thermal challenges as well. Another challenge includes expanding the number of qubits on a chip while keeping the gate fidelities the same or even better. Fabrication process control over a larger area becomes more challenging as the die size increase and problems of crosstalk between qubits could also increase with the number of qubits.

So to solve these problems we believe that quantum computers will turn to a similar solution as classical computing started using a few decades earlier and that is multiprocessing. And since everything needs a buzzword, let’s coin a new one: NISQ-MP. It is our belief this could become in an interesting solution, perhaps not in the short term, but in the medium term.

There are certain technical developments that will need to happen to make this a reality. To make NISQ-MP effective, the neighboring quantum computers will need to communicate via entangled qubits. And this is where the technology being developed for the quantum internet comes to play. The interprocessor communication may be easiest for those quantum computers based upon photonics, but might also be possible for those machines based upon other technologies, such as superconducting. In those situations, some mechanism will need to be developed to convert a superconducting qubit to a photon based qubit for transmission. And there are researchers looking at how to do this.

So in the future one can envision quantum data centers that may contain dozens or even hundreds or thousands of machines linked together by a quantum internet all within the same large room. But rather than communicating over distances of hundreds of kilometers, the average distance between nodes may only be a few meters. This short distance removes a significant problem because fiber optic cable losses will be negligible and engineers won’t need to solve the challenges associated with a long distance quantum internet such as wavelength conversion and requirements for quantum repeaters. So when thinking of a quantum internet, don’t just assume that it will be only used over long distances. The short distance links may also be very important and become a key ingredient to make NISQ-MP a reality.

In classical and quantum secure communication practical randomness is incomplete

Mon, 04 Nov 2019 16:28:51 +0000

Random bit sequences are key ingredients of various tasks in modern life and especially in secure communication. In a new study researchers have determined that generating true random bit sequences, classical or quantum, is an impossible mission. Based on these findings, they have demonstrated a new method of classified secure communication.

Evading Heisenberg isn't easy

Thu, 31 Oct 2019 15:47:42 +0000

Researchers unravel novel dynamics in the interaction between light and mechanical motion with significant implications for quantum measurements designed to evade the influence of the detector in the notorious 'back action limit' problem.

Quantum chip 1,000 times smaller than current setups

Thu, 31 Oct 2019 14:05:16 +0000

Researchers have developed a quantum communication chip that is 1,000 times smaller than current quantum setups, but offers the same superior security quantum technology is known for.

A twist and a spin: harnessing two quantum properties transforms a neutron beam into a powerful probe of material structure

12011 — Thu, 31 Oct 2019 00:00:00 +0000

Thursday, October 31, 2019

By cleverly manipulating two properties of a neutron beam, NIST scientists and their collaborators have created a powerful probe of materials that have complex and twisted magnetic structures.

My New York Times op-ed on quantum supremacy

Scott — Wed, 30 Oct 2019 11:23:30 +0000

Here it is.

I’d like to offer special thanks to the editor in charge, Eleanor Barkhorn, who commissioned this piece and then went way, way, way beyond the call of duty to get it right—including relaxing the usual length limit to let me squeeze in some amplitudes and interference, and working late into the night to fix last-minute problems. Obviously I take sole responsibility for whatever errors remain.

And while I’m posting: those of a more technical bent might want to check out my new short preprint with UT undergraduate Sam Gunn, where we directly study the complexity-theoretic hardness of spoofing Google’s linear cross-entropy benchmark using a classical computer. Enjoy!

Structured light promises path to faster, more secure communications

Tue, 29 Oct 2019 22:25:06 +0000

Quantum mechanics has come a long way during the past 100 years but still has a long way to go. Researchers now review the progress being made in using structured light in quantum protocols to create a larger encoding alphabet, stronger security and better resistance to noise.

Giving valleytronics a boost

Mon, 28 Oct 2019 14:41:29 +0000

Physicists have revealed a new quantum process in valleytronics that can speed up the development of this fairly new technology.

Our Take on Quantum Supremacy

dougfinke1 — Sat, 26 Oct 2019 21:01:51 +0000

Unless you have been living in a cave for the past week, you could not avoid all the press and hype regarding Google’s official announcement and paper in Nature magazine that describes their successful completion of their quantum supremacy experiment. And you probably also saw IBM’s rebuttal that the speedup for Google’s random benchmark experiment was not as large as Google claimed. For those of you who want to read more about this, here are some links of relevant blogs and papers covering this.

Google has been working on this experiment for quite some time. We originally heard about it in December 2017 and it was originally envisioned as using a 49 qubit chip. The design was subsequently modified to be a 72 qubit Bristlecone chip and then modified again to become the 54 qubit Sycamore chip. The underlying purpose of the experiment was to provide a mechanism to drive the engineering team to produce the best possible quantum superconducting system that they could achieve in a reasonable timeframe.

Our first observation about their chip is that is a very good chip. Not only are the number of qubits (53 working out of 54) among the leaders, but each qubit has connectivity to its four nearest neighbors. This is better than what we have seen in other superconducting designs and will allow more gate operations to occur in the same time slot. Although cross-talk can be an issue the Google design incorporated tunable couplers that helps to isolate neighboring qubits from each other when necessary. The reported single qubit, two qubit, and readout fidelities also are very competitive with a reported average single qubit error rate of 0.16%, two-qubit rates of 0.93% or less, and readout errors of 3.8%. Another impressive aspect of the design are the gate delays which are in the 10’s of nanoseconds. In order to optimize the qubit quality, Google utilized Quantum Benchmark’s True-Qsoftware for suppressing residual calibration errors and providing information on the performance of quantum gate operations.

So an obvious questions arises. If Google has a 53 qubit design and IBM has a 53 qubit design, which is better? At this point, we don’t know because we are still waiting for IBM to release additional information on their recent 53 qubit computer. But we do think that the Google hardware design sets a high bar that will make for an interesting comparison.

Some people may think that the Sycamore chip was designed specifically for this quantum supremacy experiment and would not perform as well for potential applications programs such as quantum chemistry, quantum machine learning, or optimizations. That is not the case. All the improvements that Google has incorporated into the design will also allow it to perform very well on other programs that are intended for use with NISQ quantum computers.

We have previously remarked that progress in quantum computing will spark additional innovation in classical computing and we saw this again with IBM’s response. In Google’s original paper, they estimated it would take a classical computer 10,000 years to solve the same problem, but then IBM came back and said it could be done in 2 ½ days using a different type of classical simulator that leveraged hard disks as second storage for qubit states. Although we are no sure if this specific example is highly important, because the difference between classical and quantum performance will increase dramatically on this particular benchmark as the number of qubits is increased. However, it is representative of a phenomena we have seen time and again which is: Classical computing won’t give up without a fight! Classical computing scientists are always working to improve their algorithms so beating a classical computer represents a moving target. And we expect that performance battles will continue going on forever in quantum computers, just like they have for the past 70 years with classical computers.

As impressive as Google’s achievement is, there was another achievement we reported on a month ago that may be just as important. Whereas the Google benchmark was run on a specific artificial benchmark that may have very limited commercial use, Oki Data has reported that they have used a D-Wave quantum annealer to solve a problem of commercial significance. They developed a program that optimized the manufacturing flow in their LED Management Factory that reduced the flow line distances by 28%. Oki indicated that achieving this would not be possible if they only were able to use classical computers. While some folks have compared the Google experiment to the first Wright brothers airplane flight in December 1903, perhaps we can compare the Oki Data achievement to the establishement of the first commercial airplane passenger service in January 2014. This achievement did not receive as much press as the Google announcement; perhaps because Google has a much larger PR team than Oki Data.

But to summarize our view, completion of this quantum supremacy experiment is a significant achievement and the Google team needs to be congratulated for their work. But we should ignore much of the hype floating around that states it will start creating enormous change immediately. One should view quantum computing as a marathon race and achieving this benchmark is like passing the one mile marker in a very long race. Many decades of hard work still lie ahead. Not only will the engineers and scientists need to continue to optimize the chip and hardware systems designs, but just as much work, if not more, will be needed to develop software, libraries, application program, user communities and a quantum educated workforce that can take advantage of the technology.

October 26, 2019

Small magnets: Wide-ranging impact on information technology

Fri, 25 Oct 2019 18:53:04 +0000

Physicists have identified a microscopic process of electron spin dynamics in nanoparticles that could impact the design of applications in medicine, quantum computation, and spintronics.

Extracting hidden quantum information from a light source

Thu, 24 Oct 2019 15:50:10 +0000

Researchers report on a technique to extract the quantum information hidden in an image that carries both classical and quantum information. This technique opens a new pathway for quantum enhance microscopes that aim to observe ultra-sensitive samples.

Achieving quantum supremacy

Wed, 23 Oct 2019 17:33:58 +0000

Researchers have made good on their claim to quantum supremacy. Using 53 entangled quantum bits ('qubits'), their Sycamore computer has taken on -- and solved -- a problem considered intractable for classical computers.

Quantum supremacy: the gloves are off

Scott — Wed, 23 Oct 2019 15:50:15 +0000

Links:
Google paper in Nature
New York Times article
IBM paper and blog post responding to Google’s announcement

When Google’s quantum supremacy paper leaked a month ago—not through Google’s error, but through NASA’s—I had a hard time figuring out how to cover the news here. I had to say something; on the other hand, I wanted to avoid any detailed technical analysis of the leaked paper, because I was acutely aware that my colleagues at Google were still barred by Nature‘s embargo rules from publicly responding to anything I or others said. (I was also one of the reviewers for the Nature paper, which put additional obligations on me.)

I ended up with Scott’s Supreme Quantum Supremacy FAQ, which tried to toe this impossible line by “answering general questions about quantum supremacy, and the consequences of its still-hypothetical achievement, in light of the leak.” It wasn’t an ideal solution—for one thing, because while I still regard Google’s sampling experiment as a historic milestone for our whole field, there are some technical issues, aspects that subsequent experiments (hopefully coming soon) will need to improve. Alas, the ground rules of my FAQ forced me to avoid such issues, which caused some readers to conclude mistakenly that I didn’t think there were any.

Now, though, the Google paper has come out as Nature‘s cover story, at the same time as there have been new technical developments—most obviously, the paper from IBM (see also their blog post) saying that they could simulate the Google experiment in 2.5 days, rather than the 10,000 years that Google had estimated.

(Yesterday I was deluged by emails asking me “whether I’d seen” IBM’s paper. As a science blogger, I try to respond to stuff pretty quickly when necessary, but I don’t—can’t—respond in Twitter time.)

So now the gloves are off. No more embargo. Time to address the technical stuff under the hood—which is the purpose of this post.

I’m going to assume, from this point on, that you already understand the basics of sampling-based quantum supremacy experiments, and that I don’t need to correct beginner-level misconceptions about what the term “quantum supremacy” does and doesn’t mean (no, it doesn’t mean scalability, fault-tolerance, useful applications, breaking public-key crypto, etc. etc.). If this is not the case, you could start (e.g.) with my FAQ, or with John Preskill’s excellent Quanta commentary.

Without further ado:

(1) So what about that IBM thing? Are random quantum circuits easy to simulate classically?

OK, so let’s carefully spell out what the IBM paper says. They argue that, by commandeering the full attention of Summit at Oak Ridge National Lab, the most powerful supercomputer that currently exists on Earth—one that fills the area of two basketball courts, and that (crucially) has 250 petabytes of hard disk space—one could just barely store the entire quantum state vector of Google’s 53-qubit Sycamore chip in hard disk. And once one had done that, one could simulate the chip in ~2.5 days, more-or-less just by updating the entire state vector by brute force, rather than the 10,000 years that Google had estimated on the basis of my and Lijie Chen’s “Schrödinger-Feynman algorithm” (which can get by with less memory).

The IBM group understandably hasn’t actually done this yet—even though IBM set it up, the world’s #1 supercomputer isn’t just sitting around waiting for jobs! But I see little reason to doubt that their analysis is basically right. I don’t know why the Google team didn’t consider how such near-astronomical hard disk space would change their calculations, probably they wish they had.

I find this to be much, much better than IBM’s initial reaction to the Google leak, which was simply to dismiss the importance of quantum supremacy as a milestone. Designing better classical simulations is precisely how IBM and others should respond to Google’s announcement, and how I said a month ago that I hoped they would respond. If we set aside the pass-the-popcorn PR war (or even if we don’t), this is how science progresses.

But does IBM’s analysis mean that “quantum supremacy” hasn’t been achieved? No, it doesn’t—at least, not under any definition of “quantum supremacy” that I’ve ever used. The Sycamore chip took about 3 minutes to generate the ~5 million samples that were needed to pass the “linear cross-entropy benchmark”—the statistical test that Google applies to the outputs of its device. Three minutes versus 2.5 days is still a quantum speedup by a factor of 1200. More relevant, I think, is to compare the number of “elementary operations.” Let’s generously count a FLOP (floating-point operation) as the equivalent of a quantum gate. Then by my estimate, we’re comparing ~5×10⁹ quantum gates against ~2×10²⁰ FLOPs—a quantum speedup by a factor of ~40 billion.

For me, though, the broader point is that neither party here—certainly not IBM—denies that the top-supercomputers-on-the-planet-level difficulty of classically simulating Google’s 53-qubit programmable chip really is coming from the exponential character of the quantum states in that chip, and nothing else. That’s what makes this back-and-forth fundamentally different from the previous one between D-Wave and the people who sought to simulate its devices classically. The skeptics, like me, didn’t much care what speedup over classical benchmarks there was or wasn’t today: we cared about the increase in the speedup as D-Wave upgraded its hardware, and the trouble was we never saw a convincing case that there would be one. I’m a theoretical computer scientist, and this is what I believe: that after the constant factors have come and gone, what remains are asymptotic growth rates.

In the present case, while increasing the circuit depth won’t evade IBM’s “store everything to hard disk” strategy, increasing the number of qubits will. If Google, or someone else, upgraded from 53 to 55 qubits, that would apparently already be enough to exceed Summit’s 250-petabyte storage capacity. At 60 qubits, you’d need 33 Summits. At 70 qubits, enough Summits to fill a city … you get the idea.

From the beginning, it was clear that quantum supremacy would not be a milestone like the moon landing—something that’s achieved in a moment, and is then clear to everyone for all time. It would be more like eradicating measles: it could be achieved, then temporarily unachieved, then re-achieved. For by definition, quantum supremacy all about beating something—namely, classical computation—and the latter can, at least for a while, fight back.

As Boaz Barak put it to me, the current contest between IBM and Google is analogous to Kasparov versus Deep Blue—except with the world-historic irony that IBM is playing the role of Kasparov! In other words, Kasparov can put up a heroic struggle, during a “transitional period” that lasts a year or two, but the fundamentals of the situation are that he’s toast. If Kasparov had narrowly beaten Deep Blue in 1997, rather than narrowly losing, the whole public narrative would likely have been different (“humanity triumphs over computers after all!”). Yet as Kasparov himself well knew, the very fact that the contest was close meant that, either way, human dominance was ending.

Let me leave the last word on this to friend-of-the-blog Greg Kuperberg, who graciously gave me permission to quote his comments about the IBM paper.

I’m not entirely sure how embarrassed Google should feel that they overlooked this. I’m sure that they would have been happier to anticipate it, and happier still if they had put more qubits on their chip to defeat it. However, it doesn’t change their real achievement.
I respect the IBM paper, even if the press along with it seems more grouchy than necessary. I tend to believe them that the Google team did not explore all avenues when they said that their 53 qubits aren’t classically simulable. But if this is the best rebuttal, then you should still consider how much Google and IBM still agree on this as a proof-of-concept of QC. This is still quantum David vs classical Goliath, in the extreme. 53 qubits is in some ways still just 53 bits, only enhanced with quantum randomness. To answer those 53 qubits, IBM would still need entire days of computer time with the world’s fastest supercomputer, a 200-petaflop machine with hundreds of thousands of processing cores and trillions of high-speed transistors. If we can confirm that the Google chip actually meets spec, but we need this much computer power to do it, then to me that’s about as convincing as a larger quantum supremacy demonstration that humanity can no longer confirm at all.
Honestly, I’m happy to give both Google and IBM credit for helping the field of QC, even if it is the result of a strange dispute.

I should mention that, even before IBM’s announcement, Johnnie Gray, a postdoc at Imperial College, gave a talk (abstract here) at Caltech’s Institute for Quantum Information with a proposal for a different faster way to classically simulate quantum circuits like Google’s—in this case, by doing tensor network contraction more cleverly. Unlike both IBM’s proposed brute-force simulation, and the Schrödinger-Feynman algorithm that Google implemented, Gray’s algorithm (as far as we know now) would need to be repeated k times if you wanted k independent samples from the hard distribution. Partly because of this issue, Gray’s approach doesn’t currently look competitive for simulating thousands or millions of samples, but we’ll need to watch it and see what happens.

(2) Direct versus indirect verification.

The discussion of IBM’s proposed simulation brings us to a curious aspect of the Google paper—one that was already apparent when Nature sent me the paper for review back in August. Namely, Google took its supremacy experiments well past the point where even they themselves knew how to verify the results, by any classical computation that they knew how to perform feasibly (say, in less than 10,000 years).

So you might reasonably ask: if they couldn’t even verify the results, then how did they get to claim quantum speedups from those experiments? Well, they resorted to various gambits, which basically involved estimating the fidelity on quantum circuits that looked almost the same as the hard circuits, but happened to be easier to simulate classically, and then making the (totally plausible) assumption that that fidelity would be maintained on the hard circuits. Interestingly, they also cached their outputs and put them online (as part of the supplementary material to their Nature paper), in case it became feasible to verify them in the future.

Maybe you can now see where this is going. From Google’s perspective, IBM’s rainstorm comes with a big silver lining. Namely, by using Summit, hopefully it will now be possible to verify Google’s hardest (53-qubit and depth-20) sampling computations directly! This should provide an excellent test, since not even the Google group themselves would’ve known how to cheat and bias the results had they wanted to.

This whole episode has demonstrated the importance, when doing a sampling-based quantum supremacy experiment, of going deep into the regime where you can no longer classically verify the outputs, as weird as that sounds. Namely, you need to leave yourself a margin, in the likely event that the classical algorithms improve!

Having said that, I don’t mind revealing at this point that the lack of direct verification of the outputs, for the largest reported speedups, was my single biggest complaint when I reviewed Google’s Nature submission. It was because of my review that they added a paragraph explicitly pointing out that they did do direct verification, using something like a million cores running for something like a month, for a smaller quantum speedup (merely a million times faster than a Schrödinger-Feynman simulation running on a million cores, rather than two billion times faster).

(3) The asymptotic hardness of spoofing Google’s benchmark.

OK, but if Google thought that spoofing its test would take 10,000 years, using the best known classical algorithms running on the world’s top supercomputers, and it turns out instead that it could probably be done in more like 2.5 days, then how much else could’ve been missed? Will we find out next that Google’s benchmark can be classically spoofed in mere milliseconds?

Well, no one can rule that out, but we do have some reasons to think that it’s unlikely—and crucially, that even if it turned out to be true, one would just have to add 10 or 20 or 30 more qubits to make it no longer true. (We can’t be more definitive than that? Aye, such are the perils of life at a technological inflection point—and of computational complexity itself.)

The key point to understand here is that we really are talking about simulating a random quantum circuit, with no particular structure whatsoever. While such problems might have a theoretically efficient classical algorithm—i.e., one that runs in time polynomial in the number of qubits—I’d personally be much less surprised if you told me there was a polynomial-time classical algorithm for factoring. In the universe where amplitudes of random quantum circuits turn out to be efficiently computable—well, you might as well just tell me that P=PSPACE and be done with it.

Crucially, if you look at IBM’s approach to simulating quantum circuits classically, and Johnnie Gray’s approach, and Google’s approach, they could all be described as different flavors of “brute force.” That is, they all use extremely clever tricks to parallelize, shave off constant factors, make the best use of available memory, etc., but none involves any deep new mathematical insight that could roust BPP and BQP and the other complexity gods from their heavenly slumber. More concretely, none of these approaches seem to have any hope of “breaching the 2ⁿ barrier,” where n is the number of qubits in the quantum circuit to be simulated (assuming that the circuit depth is reasonably large). Mostly, they’re just trying to get down to that barrier.

Ah, but at the end of the day, we only believe that Google’s Sycamore chip is solving a classically hard problem because of the statistical test that Google applies to its outputs: the so-called “Linear Cross-Entropy Benchmark,” which I described in Q3 of my FAQ. And even if we grant that calculating the output probabilities for a random quantum circuit is almost certainly classically hard, and sampling the output distribution of a random quantum circuit is almost certainly classically hard—still, couldn’t spoofing Google’s benchmark be classically easy?

This last question is where complexity theory can contribute something to story. A couple weeks ago, UT undergraduate Sam Gunn and I adapted the hardness analysis from my and Lijie Chen’s 2017 paper “Complexity-Theoretic Foundations of Quantum Supremacy Experiments,” to talk directly about the classical hardness of spoofing the Linear Cross-Entropy benchmark. Our short paper about this should be on the arXiv later this week. Briefly, though, Sam and I show that if you had a sub-2ⁿ classical algorithm to spoof the Linear Cross-Entropy benchmark, then you’d also have a sub-2ⁿ classical algorithm that, given as input a random quantum circuit, could estimate a specific output probability (for example, that of the all-0 string) with variance at least slightly (say, Ω(2^-3n)) better than that of the trivial estimator that just always guesses 2^-n. Or in other words: spoofing Google’s benchmark is no easier than the general problem of nontrivially estimating amplitudes in random quantum circuits. Our result helps to explain why, indeed, neither IBM nor Johnnie Gray nor anyone else has suggested any attack that’s specific to Google’s Linear Cross-Entropy benchmark: they all simply attack the general problem of calculating the final amplitudes.

(4) Why use Linear Cross-Entropy at all?

In the comments of my FAQ, some people wondered why Google chose the Linear Cross-Entropy benchmark specifically—especially since they’d used a different benchmark (multiplicative cross-entropy, which unlike the linear version actually is a cross-entropy) in their earlier papers. I asked John Martinis this question, and his answer was simply that linear cross-entropy had the lowest variance of any estimator they tried. Since I also like linear cross-entropy—it turns out, for example, to be convenient for the analysis of my certified randomness protocol—I’m 100% happy with their choice. Having said that, there are many other choices of benchmark that would’ve also worked fine, and with roughly the same level of theoretical justification.

(5) Controlled-Z versus iSWAP gates.

Another interesting detail from the Google paper is that, in their previous hardware, they could implement a particular 2-qubit gate called the Controlled-Z. For their quantum supremacy demonstration, on the other hand, they modified their hardware to implement a different 2-qubit gate called the ~~iSWAP~~ some weird combination of iSWAP and Controlled-Z; see the comments section for more. Now, this other gate has no known advantages over the Controlled-Z, for any applications like quantum simulation or Shor’s algorithm or Grover search. Why then did Google make the switch? Simply because, with certain classical simulation methods that they’d been considering, the simulation’s running time grows like 4 to the power of the number of these other gates, but only like 2 to the power of the number of Controlled-Z gates! In other words, they made this engineering choice purely and entirely to make a classical simulation of their device sweat more. This seems totally fine and entirely within the rules to me. (Alas, this choice has no effect on a proposed simulation method like IBM’s.)

(6) Gil Kalai’s objections.

Over the past month, Shtetl-Optimized regular and quantum computing skeptic Gil Kalai has been posting one objection to the Google experiment after another on his here.

Blanket of light may give better quantum computers

Thu, 17 Oct 2019 18:11:08 +0000

Researchers describe how -- by simple means -- they have created a 'carpet' of thousands of quantum-mechanically entangled light pulses. The discovery has the potential to pave the way for more powerful quantum computers.

Weaving quantum processors out of laser light

Thu, 17 Oct 2019 18:10:35 +0000

Researchers open a new avenue to quantum computing with a breakthrough experiment: a large-scale quantum processor made entirely of light.

Three C&O professors are awarded NSERC Discovery Accelerator Supplements

18348 — Thu, 17 Oct 2019 00:00:00 +0000

Thursday, October 17, 2019

Three C&O faculty members have been awarded 2019 NSERC Discovery Accelerator Supplements (DAS):

David Gosset: Algorithms and complexity for quantum advantage
Luke Postle: Graph colouring and local algorithms

Diversity may be key to reducing errors in quantum computing

Tue, 15 Oct 2019 14:34:11 +0000

In quantum computing, as in team building, a little diversity can help get the job done better, computer scientists have discovered.

Quantum paradox experiment may lead to more accurate clocks and sensors

Tue, 15 Oct 2019 13:22:42 +0000

More accurate clocks and sensors may result from a recently proposed experiment, linking an Einstein-devised paradox to quantum mechanics. A physicist said the international collaboration aimed to test Einstein's twin paradox using quantum particles in a 'superposition' state.

IQC Achievement Award winner announced

12011 — Tue, 15 Oct 2019 00:00:00 +0000

Tuesday, October 15, 2019

Mária Kieferová talks quantum algorithms, studying a PhD at two universities and keeping up with industry.

Controlling superconducting regions within an exotic metal

Fri, 11 Oct 2019 11:47:17 +0000

Researchers have created a metallic microdevice in which they can define and tune patterns of superconductivity. Their discovery holds great promise for quantum technologies of the future.

New material could someday power quantum computer

Thu, 10 Oct 2019 18:21:30 +0000

Quantum computers with the ability to perform complex calculations, encrypt data more securely and more quickly predict the spread of viruses, may be within closer reach thanks to a new discovery.

Physicists couple key components of quantum technologies

Wed, 09 Oct 2019 14:32:59 +0000

Researchers are engaged in intensive work on the components of quantum technologies - these include circuits processing information using single photons instead of electricity, as well as light sources producing such quanta of light. Coupling these components to produce integrated quantum optical circuits on chips presents a challenge. Researchers have developed an interface that couples light sources for single photons with nanophotonic networks consisting of photonic crystals which can be replicated by using established nanofabrication processes.

Scientists observe a single quantum vibration under ordinary conditions

Tue, 08 Oct 2019 15:59:12 +0000

Scientists have for the first time created and observed a single phonon in a common material at room temperature.

Cooling nanotube resonators with electrons

Tue, 08 Oct 2019 14:46:49 +0000

Researchers report on a technique that uses electron transport to cool a nanomechanical resonator near the quantum regime.

Book Review: ‘The AI Does Not Hate You’ by Tom Chivers

Scott — Mon, 07 Oct 2019 02:09:05 +0000

A couple weeks ago I read The AI Does Not Hate You: Superintelligence, Rationality, and the Race to Save the World, the first-ever book-length examination of the modern rationalist community, by British journalist Tom Chivers. I was planning to review it here, before it got preempted by the news of quantum supremacy (and subsequent news of classical non-supremacy). Now I can get back to rationalists.

Briefly, I think the book is a triumph. It’s based around in-person conversations with many of the notable figures in and around the rationalist community, in its Bay Area epicenter and beyond (although apparently Eliezer Yudkowsky only agreed to answer technical questions by Skype), together of course with the voluminous material available online. There’s a good deal about the 1990s origins of the community that I hadn’t previously known.

The title is taken from Eliezer’s aphorism, “The AI does not hate you, nor does it love you, but you are made of atoms which it can use for something else.” In other words: as soon as anyone succeeds in building a superhuman AI, if we don’t take extreme care that the AI’s values are “aligned” with human ones, the AI might be expected to obliterate humans almost instantly as a byproduct of pursuing whatever it does value, more-or-less as we humans did with woolly mammoths, moas, and now gorillas, rhinos, and thousands of other species.

Much of the book relates Chivers’s personal quest to figure out how seriously he should take this scenario. Are the rationalists just an unusually nerdy doomsday cult? Is there some non-negligible chance that they’re actually right about the AI thing? If so, how much more time do we have—and is there even anything meaningful that can be done today? Do the dramatic advances in machine learning over the past decade change the outlook? Should Chivers be worried about his own two children? How does this risk compare to the more “prosaic” civilizational risks, like climate change or nuclear war? I suspect that Chivers’s exploration will be most interesting to readers who, like me, regard the answers to none of these questions as obvious.

While it sounds extremely basic, what makes The AI Does Not Hate You so valuable to my mind is that, as far as I know, it’s nearly the only examination of the rationalists ever written by an outsider that tries to assess the ideas on a scale from true to false, rather than from quirky to offensive. Chivers’s own training in academic philosophy seems to have been crucial here. He’s not put off by people who act weirdly around him, even needlessly cold or aloof, nor by utilitarian thought experiments involving death or torture or weighing the value of human lives. He just cares, relentlessly, about the ideas—and about remaining a basically grounded and decent person while engaging them. Most strikingly, Chivers clearly feels a need—anachronistic though it seems in 2019—actually to understand complicated arguments, be able to repeat them back correctly, before he attacks them.

Indeed, far from failing to understand the rationalists, it occurs to me that the central criticism of Chivers’s book is likely to be just the opposite: he understands the rationalists so well, extends them so much sympathy, and ends up endorsing so many aspects of their worldview, that he must simply be a closet rationalist himself, and therefore can’t write about them with any pretense of journalistic or anthropological detachment. For my part, I’d say: it’s true that The AI Does Not Hate You is what you get if you treat rationalists as extremely smart (if unusual) people from whom you might learn something of consequences, rather than as monkeys in a zoo. On the other hand, Chivers does perform the journalist’s task of constantly challenging the rationalists he meets, often with points that (if upheld) would be fatal to their worldview. One of the rationalists’ best features—and this precisely matches my own experience—is that, far from clamming up or storming off when faced with such challenges (“lo! the visitor is not one of us!”), the rationalists positively relish them.

It occurred to me the other day that we’ll never know how the rationalists’ ideas would’ve developed, had they continued to do so in a cultural background like that of the late 20th century. As Chivers points out, the rationalists today are effectively caught in the crossfire of a much larger cultural war—between, to their right, the recrudescent know-nothing authoritarians, and to their left, what one could variously describe as woke culture, call-out culture, or sneer culture. On its face, it might seem laughable to conflate the rationalists with today’s resurgent fascists: many rationalists are driven by their utilitarianism to advocate open borders and massive aid to the Third World; the rationalist community is about as welcoming of alternative genders and sexualities as it’s humanly possible to be; and leading rationalists like Scott Alexander and Eliezer Yudkowsky strongly condemned Trump for the obvious reasons.

Chivers, however, explains how the problem started. On rationalist Internet forums, many misogynists and white nationalists and so forth encountered nerds willing to debate their ideas politely, rather than immediately banning them as more mainstream venues would. As a result, many of those forces of darkness (and they probably don’t mind being called that) predictably congregated on the rationalist forums, and their stench predictably wore off on the rationalists themselves. Furthermore, this isn’t an easy-to-fix problem, because debating ideas on their merits, extending charity to ideological opponents, etc. is the rationalists’ entire shtick, whereas denouncing and no-platforming anyone who can be connected to an ideological enemy (in the modern parlance, “punching Nazis”) is the entire shtick of those who today condemn the rationalists.

Compounding the problem is that, as anyone who’s ever hung out with STEM nerds might’ve guessed, the rationalist community tends to skew WASP, Asian, or Jewish, non-impoverished, and male. Worse yet, while many rationalists live their lives in progressive enclaves and strongly support progressive values, they’ll also undergo extreme anguish if they feel forced to subordinate truth to those values.

Chivers writes that all of these issues “blew up in spectacular style at the end of 2014,” right here on this blog. Oh, what the hell, I’ll just quote him:

Scott Aaronson is, I think it’s fair to say, a member of the Rationalist community. He’s a prominent theoretical computer scientist at the University of Texas at Austin, and writes a very interesting, maths-heavy blog called Shtetl-Optimised.
People in the comments under his blog were discussing feminism and sexual harassment. And Aaronson, in a comment in which he described himself as a fan of Andrea Dworkin, described having been terrified of speaking to women as a teenager and young man. This fear was, he said, partly that of being thought of as a sexual abuser or creep if any woman ever became aware that he sexually desired them, a fear that he picked up from sexual-harassment-prevention workshops at his university and from reading feminist literature. This fear became so overwhelming, he said in the comment that came to be known as Comment #171, that he had ‘constant suicidal thoughts’ and at one point ‘actually begged a psychiatrist to prescribe drugs that would chemically castrate me (I had researched which ones), because a life of mathematical asceticism was the only future that I could imagine for myself.’ So when he read feminist articles talking about the ‘male privilege’ of nerds like him, he didn’t recognise the description, and so felt himself able to declare himself ‘only’ 97 per cent on board with the programme of feminism.
It struck me as a thoughtful and rather sweet remark, in the midst of a long and courteous discussion with a female commenter. But it got picked up, weirdly, by some feminist bloggers, including one who described it as ‘a yalp of entitlement combined with an aggressive unwillingness to accept that women are human beings just like men’ and that Aaronson was complaining that ‘having to explain my suffering to women when they should already be there, mopping my brow and offering me beers and blow jobs, is so tiresome.’
Scott Alexander (not Scott Aaronson) then wrote a furious 10,000-word defence of his friend… (p. 214-215)

And then Chivers goes on to explain Scott Alexander’s central thesis, in Untitled, that privilege is not a one-dimensional axis, so that (to take one example) society can make many women in STEM miserable while also making shy male nerds miserable in different ways.

For nerds, perhaps an alternative title for Chivers’s book could be “The Normal People Do Not Hate You (Not All of Them, Anyway).” It’s as though Chivers is demonstrating, through understated example, that taking delight in nerds’ suffering, wanting them to be miserable and alone, mocking their weird ideas, is not simply the default, well-adjusted human reaction, with any other reaction being ‘creepy’ and ‘problematic.’ Some might even go so far as to apply the latter adjectives to the sneerers’ attitude, the one that dresses up schoolyard bullying in a social-justice wig.

Reading Chivers’s book prompted me to reflect on my own relationship to the rationalist community. For years, I interacted often with the community—I’ve known Robin Hanson since ~2004 and Eliezer Yudkowsky since ~2006, and our blogs bounced off each other—but I never considered myself a member. I never ranked paperclip-maximizing AIs among humanity’s more urgent threats—indeed, I saw them as a distraction from an all-too-likely climate catastrophe that will leave its survivors lucky to have stone tools, let alone AIs. I was also repelled by what I saw as the rationalists’ cultier aspects. I even once toyed with the idea of changing the name of this blog to “More Wrong” or “Wallowing in Bias,” as a play on the rationalists’ LessWrong and OvercomingBias.

But I’ve drawn much closer to the community over the last few years, because of a combination of factors:

The comment-171 affair. This was not the sort of thing that could provide any new information about the likelihood of a dangerous AI being built, but was (to put it mildly) the sort of thing that can tell you who your friends are. I learned that empathy works a lot like intelligence, in that those who boast of it most loudly are often the ones who lack it.
The astounding progress in deep learning and reinforcement learning and GANs, which caused me (like everyone else, perhaps) to update in the direction of human-level AI in our lifetimes being an actual live possibility,
The rise of Scott Alexander. To the charge that the rationalists are a cult, there’s now the reply that Scott, with his constant equivocations and doubts, his deep dives into data, his clarity and self-deprecating humor, is perhaps the least culty cult leader in human history. Likewise, to the charge that the rationalists are basement-dwelling kibitzers who accomplish nothing of note in the real world, there’s now the reply that Scott has attracted a huge mainstream following (Steven Pinker, Paul Graham, presidential candidate Andrew Yang…), purely by offering up what’s self-evidently some of the best writing of our time.
Research. MIRI (the Machine Intelligence Research Institute) and OpenAI are now publishing some research papers that I find interesting—some with relatively approachable problems that I could see myself trying to think about if quantum computing ever got boring. This shift seems to have happened at roughly around the same time my former student, Paul Christiano, “defected” from quantum computing to AI-risk research.

Anyway, if you’ve spent years steeped in the rationalist blogosphere, read Eliezer’s “Sequences,” and so on, The AI Does Not Hate You will probably have little that’s new, although it might still be interesting to revisit ideas and episodes that you know through a newcomer’s eyes. To anyone else … well, reading the book will be a lot faster than spending all those years reading blogs! I’ve heard of some rationalists now giving out copies of the book to their relatives, by way of explaining how they’ve chosen to spend their lives.

I still don’t know whether there’s a risk worth worrying about that a misaligned AI will threaten human civilization in my lifetime, or my children’s lifetimes, or even 500 years—or whether everyone will look back and laugh at how silly some people once were to think that (except, silly in which way?). But I do feel fairly confident that The AI Does Not Hate You will make a positive difference—possibly for the world, but at any rate for a little well-meaning community of sneered-at nerds obsessed with the future and with following ideas wherever they lead.

Next-generation single-photon source for quantum information science

Sat, 05 Oct 2019 17:40:20 +0000

Researchers have built what they believe is 'the world's most efficient single-photon source.' And they are still improving it. With planned upgrades, the apparatus could generate upwards of 30 photons at unprecedented efficiencies. Sources of that caliber are precisely what's needed for optical quantum information applications.

Quantum Xchange Introduces a New Type of Quantum Resistant Key Management System

dougfinke1 — Fri, 04 Oct 2019 19:56:51 +0000

Quantum Xchange, one of the pioneers in providing commercial quantum key distribution services in the United States, has introduced a new product called Phio Trusted Xchange (TX). This system uses an out-of-band symmetric key technique to deliver encryption keys. Because the communication of the keys is separated from communication of the data, the Phio TX’s protection stems from the fact that anyone intercepting the keys and data would need to match up which piece of data goes with which key. That may be an impossible task since these two elements may be transmitted at different times and mixed up with other communications traffic. This system uses a patent pending technology and is different from any of the Post-Quantum Cryptography (PQC) methods that are under review in the NIST competition.

Quantum Xchange views their Phio TX product as complementary to their previously released QKD-based Phio QK system. Because Phio TX uses classical transmission media for communication, this system is not distance limited in the way that QKD transmissions are over fiber optic cable. However, for distances that are less than 100 km, the Phio QK system does provides unbreakable key distribution that is guaranteed by the laws of quantum mechanics and Quantum Xchange does have production customers using it within their U.S. East Coast network.

Quantum Xchange has provided us with a diagram of their high level architecture which you can see below.

Source: Quantum Xchange

For more information regarding Quantum Xchange’s announcement, you can view the news release on their web site here.

Quantum Gold Rush: The Private Funding Pouring into Quantum Start-ups

dougfinke1 — Fri, 04 Oct 2019 18:50:47 +0000

Nature magazine has published a comprehensive and well-researched article that explores venture funding in quantum technology. The article includes interviews with several of the industry’s entrepreneurs, information on patents, and classification of investments by year, location, and technology segment. You can view the article on their web site here. They also include a supplementary table that documents 88 separate deals since 2012 that you can find here.

For anyone who is either trying to raise capital or considering an investment, this article is well worth reading.

October 4, 2019

News 2019

dougfinke1 — Thu, 03 Oct 2019 21:08:38 +0000

How to Tell if Your Quantum Computer is Any Good. Lessons from the Sports Car Industry

dougfinke1 — Thu, 03 Oct 2019 19:20:19 +0000

With the recent leaks about Google’s quantum supremacy experiment, it is worth asking the question of how can one measure how well a machine is doing. There are multiple ways of doing this and we thought it would instructive to see how sports car manufacturers do it.

Sports car manufacturers use a few different techniques to get feedback on their automobiles and typically use a mixture of the techniques described below. Engineers working on quantum hardware also use multiple ways of getting feedback on their performance and at a high level there are similarities to auto manufacturers in the basic approaches they may take.

Parametric Approach
One approach is to develop specific component metrics, take measurements and work to improve them. In the automotive world these would include dynamometer test to measure horsepower, wind tunnel tests to measure drag coefficients, and skip pad testing to test lateral acceleration. Although getting good results on these tests may not directly reflect upon how satisfied a particular customer will be when buying the car, it is safe to say the better one does on these tests, the more disposed their customers will be to like the car. Read this blog article to see how Mercedes-AMG does it.

Comparable parameters in quantum computing might include the T1 and T2 coherence times, single qubit and two-qubit gate fidelities. IBM has been promoting a parameter called Quantum Volume which takes into account parameters such as qubit count, gate fidelities, connectivity, and others. But Quantum Volume does not actually test the machine at the application level.

Nürburgring Approach
In Germany, there is a famous race track called Nürburgring that almost all the world’s automakers use to test out their cars. It consists of about 13 miles of varied roadway including straightaways, turns, and hills and is rented out about 19 weeks per year by an Industry Pool of about 40 car manufacturers and their suppliers so they can test out their new cars and technology. A key component of the testing is to make sure the cars have the durability to hold up under harsh usage. Doing well on the Nürburgring circuit may not necessarily be called a “real world” example since the average customer will never have a chance to drive their cars there. But if a car can do perform well on Nürburgring, it is highly likely that it will also have good performance on the challenging curves of roads like California’s Highway 1. This article from Autoweek provides more detail on how the automakers utilize Nürburgring for their development efforts.

So we might call the Nürburgring Approach a “non-real-world application level benchmark”. We could also call Google’s Quantum Supremacy experiment the same thing. Although there may not be much commercial use for the Quantum Supremacy test which is based upon determining the output of a random quantum circuit, it is highly likely that applications such as quantum machine learning, computational chemistry, and optimizations will greatly benefit from the design improvements that were made in order to successfully complete the quantum supremacy demonstration.

Customer Feedback Approach
Of course, a manufacturer performing their own tests on an automobile or computer may not always be enough. Sometimes end customers have concerns or issues that a manufacturer had not thought of or even tested. So for this reason, they hold customer feedback sessions. Sometime it may just consist of online customer surveys, other times it may consist of specially organized focus group, and occasionally they may even bring potential customers out to a test track to drive a prototype car and get their responses. This article from InterQ Research describes how Tesla used focus groups to design their Model X SUV.

One quantum company that has developed features based upon customer feedback is D-Wave. Features that they introduced in the D-Wave 2000Q such as anneal offsets, reverse annealing, anneal quench and others resulted in discussions with users. And some of the new features such as the improved qubit connectivity and lower noise qubits that will be available in their upcoming Advantage system were also encouraged by their interactions with customers.

Conclusion
So there is a lot that can be learned from the sports car industry (as well as others) on how they measure performance and get feedback to guide design improvements in their products. Although we have given specific examples of IBM using the Parametric Approach, Google using the Nürburgring Approach, and D-Wave using the Customer Feedback Approach, all of these companies use a mixture of ways to get a reading on how well their machines are performing and we recommend that they continue to do so because no one single method can provide all the information needed to help one develop the best possible machine.

Keeping cool with quantum wells

Thu, 03 Oct 2019 11:48:51 +0000

A research team has invented a semiconductor quantum well system that can efficiently cool electronic devices using established fabrication methods. This work can allow for smaller and faster smart devices that consume less power.

From quantum supremacy to classical fallacy

Scott — Thu, 03 Oct 2019 03:59:37 +0000

Maybe I should hope that people never learn to distinguish for themselves which claimed breakthroughs in building new forms of computation are obviously serious, and which ones are obviously silly. For as long as they don’t, this blog will always serve at least one purpose. People will cite it, tweet it, invoke its “authority,” even while from my point of view, I’m offering nothing more intellectually special than my toddler does when he calls out “moo-moo cow! baa-baa sheep!” as we pass them on the road.

But that’s too pessimistic. Sure, most readers must more-or-less already know what I’ll say about each thing: that Google’s quantum supremacy claim is serious, memcomputing to solve NP-complete problems is not, etc. Even so, I’ve heard from many readers that this blog was at least helpful for double-checking their initial impressions, and for making common knowledge what before had merely been known to many. I’m fine for it to continue serving those roles.

Last week, even as I dealt with fallout from Google’s quantum supremacy leak, I also got several people asking me to comment on a Nature paper entitled Integer factorization using stochastic magnetic tunnel junctions (warning: paywalled). See also here for a university press release.

The authors report building a new computer based on asynchronously updated “p-bits” (probabilistic bits): “a robust, classical entity fluctuating in time between 0 and 1, which interacts with other p-bits … using principles inspired by neural networks.” They build a device with 8 p-bits, and use it to factor integers up to 945. They present this as another “unconventional computation scheme” alongside quantum computing, and as a “potentially scalable hardware approach to the difficult problems of optimization and sampling.”

A commentary accompanying the Nature paper goes much further still—claiming that the new factoring approach, “if improved, could threaten data encryption,” and that resources should now be diverted from quantum computing to this promising new idea, one with the advantages of requiring no refrigeration or maintenance of delicate entangled states. (It should’ve added: and how big a number has Shor’s algorithm factored anyway, 21? Compared to 945, that’s peanuts!)

Since I couldn’t figure out a gentler way to say this, here goes: it’s astounding that this paper and commentary made it into Nature in the form that they did. Juxtaposing Google’s sampling achievement with p-bits, as several of my Facebook friends did last week, is juxtaposing the Wright brothers with some guy bouncing around on a pogo stick.

If you were looking forward to watching me dismantle this, then I’m sorry to disappoint: the task is over almost the moment it begins. “p-bit” devices can’t scalably outperform classical computers, for the simple reason that they are classical computers. A little unusual in their architecture, but still well-covered by the classical Extended Church-Turing Thesis. Just like with the quantum adiabatic algorithm, an energy penalty is applied to coax the bits into running a local optimization algorithm: that is, making random local moves that preferentially decrease the number of violated constraints. Except here, because the whole evolution is classical, there doesn’t seem to be even the pretense that anything is happening that a laptop with a random-number generator couldn’t straightforwardly simulate. In terms of this editorial, if adiabatic quantum computing is Richard Nixon—hiding its lack of observed speedups behind subtle arguments about tunneling and spectral gaps—then p-bit computing is Trump.

Even so, I wouldn’t be writing this post if you opened the paper and it immediately said, “look, we know. You’re thinking that this is just yet another stochastic local optimization method, which could clearly be simulated efficiently on a conventional computer, putting it into a different conceptual universe from quantum computing. You’re thinking that factoring an n-bit integer will self-evidently take exp(n) time by this method, as compared to exp(n^1/3) for the Number Field Sieve, and that no crypto is in even remote danger from this. But here’s why you should still be interested in our p-bit model: because it has other advantages X, Y, and Z.” Alas, in vain one searches the whole paper, and the lengthy supplementary material, and the commentary, for any acknowledgment of the pachyderm in the pagoda. Not an asymptotic runtime scaling in sight. Quantum computing is there, but stripped of the theoretical framework that gives it its purpose.

That silence, in the pages of Nature—that’s the part that convinced me that, while on the negative side this blog seems to have accomplished nothing for the world in 14 years of existence, on the positive side it will likely have a role for decades to come.

(Partly) Unrelated Announcement #1: My new postdoc, Andrea Rocchetto, had the neat idea of compiling a Quantum Computing Fact Sheet: a quick “Cliffs Notes” for journalists, policymakers, and others looking to get the basics right. The fact sheet might grow in the future, but in the meantime, check it out!

Unrelated Announcement #2: Daniel Wichs asked me to give a shout-out to a new Conference on Information-Theoretic Cryptography, to be held June 17-19 in Boston.

Tunable optical chip paves way for new quantum devices

Wed, 02 Oct 2019 16:17:21 +0000

Researchers have created a silicon carbide (SiC) photonic integrated chip that can be thermally tuned by applying an electric signal. The approach could one day be used to create a large range of reconfigurable devices such as phase-shifters and tunable optical couplers needed for networking applications and quantum information processing.

2000 atoms in two places at once

Wed, 02 Oct 2019 11:59:29 +0000

The quantum superposition principle has been tested on a scale as never before in a new study. Hot, complex molecules composed of nearly two thousand atoms were brought into a quantum superposition and made to interfere. By confirming this phenomenon -- 'the heart of quantum mechanics', in Richard Feynman's words -- on a new mass scale, improved constraints on alternative theories to quantum mechanics have been placed.