Quantum

QuSecure Wins SBIR Phase III Contract from the U.S. Government for Post-Quantum Cybersecurity

dougfinke — Sun, 03 Jul 2022 01:23:39 +0000

QuSecure, a company founded in 2019 and located in San Mateo, California, has secured a SBIR Phase III contract to implement their QuProtect post-quantum cryptographic solution to more than a dozen Federal Government agencies. Previously the company had started with a Phase I award from the U.S. Air Force in March 2020 to demonstrate their [...]

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Another Metropolitan Quantum Network Being Set Up in the Washington DC Area

dougfinke — Sat, 02 Jul 2022 21:53:20 +0000

A new metropolitan quantum network is being formed to join ones already started in Chicago, Long Island New York, and London. This one will be in the Washington DC area and will be called the Washington Metropolitan Quantum Network Research Consortium (DC-QNet). It will includes six U.S. government agencies located in the area and two [...]

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qBraid Announces an Integration of their qBraid Lab SDK with Amazon Braket

dougfinke — Sat, 02 Jul 2022 21:08:50 +0000

The qBraid Lab SDK created by qBraid has three interesting features that can make it easier and faster for an end user to run a circuit on multiple platforms. The first is a "write-once-and-submit" function which can allow a user to create a circuit and submit it to multiple quantum hardware platforms and simulators without [...]

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NIST to Announce PQC Algorithm Selections on July 5, 2022

dougfinke — Sat, 02 Jul 2022 04:41:03 +0000

NIST has indicated that they will announce which of the Round 3 Post Quantum Cryptography algorithms they will select for standardization and also which of the alternatives will proceed on to Round 4 for analysis on July 5, 2022. This will represent a major milestone in a process that was started in late 2016. NIST [...]

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Research Roundup for June 2022

dougfinke — Sat, 02 Jul 2022 03:41:56 +0000

By Dr. Chris Mansell Hardware Title: Quantum computational advantage with a programmable photonic processorOrganizations: Xanadu; National Institute of Standards and TechnologyUntil the publication of this latest work, none of the photonic quantum processors that achieved a computational advantage over classical computers could be programmed and their advantages started being called into question by new classical [...]

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Advocating a new paradigm for electron simulations

Fri, 01 Jul 2022 15:31:47 +0000

Researchers improve a widely used simulation method for high-performance computing clusters.

Chicago’s Duality Quantum Accelerator Selects Its Second Cohort

dougfinke — Fri, 01 Jul 2022 05:00:14 +0000

Chicago's Duality Quantum Accelerator, led by the Polsky Center for Entrepreneurship and Innovation at the University of Chicago and the Chicago Quantum Exchange with additional founding partners the University of Illinois Urbana-Champaign, Argonne National Laboratory, and P33, has selected its second set of Cohort 2 startup companies for the next 12 month program starting on [...]

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How Should Quantum Computations Be Priced?

dougfinke — Wed, 29 Jun 2022 23:44:40 +0000

By Andre Saraiva, Diraq For the impatient reader, here are the oversimplified answers to the questions we aim to address in this article: “How do I put a price to quantum computation?” The resources needed are 1) qubits and 2) runtime. The unit of quantum computation should be the qubit-second. However, at the moment, that [...]

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Planqc Secures €4.6 Million ($4.82M USD) Funding Round for Development of a Neutral Atom Based Quantum Computer

dougfinke — Wed, 29 Jun 2022 19:27:57 +0000

Munich based planqc is a spinout from the Max-Planck-Institute of Quantum Optics and Ludwig-Maximilians-University Munich that is developing a room temperature quantum computer based on neutral atoms trapped in optical lattices. The financing round was led by UVC Partners and Speedinvest. The company is the first startup to emerge from the Munich Quantum Valley. They [...]

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Tracking a levitated nanoparticle with a mirror

Wed, 29 Jun 2022 14:22:55 +0000

Sensing with levitated nanoparticles has so far been limited by the precision of position measurements. Now, researchers have demonstrated a new method for optical interferometry in which light scattered by a particle is reflected by a mirror. This opens up new possibilities for using levitated particles as sensors, in particular, in quantum regimes.

SEMI Japan Forms a New Quantum Computer Council

dougfinke — Tue, 28 Jun 2022 19:05:55 +0000

The Semiconductor Industry Association of Japan (SEMI Japan) has formed a new council with a purpose of to promote communication and information sharing between SEMI member companies and those involved in quantum computers regarding quantum computers. The initial members include ten domestic semiconductor supply chain companies and research institutes and organizations with additional members expected [...]

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Rigetti and Riverlane Receive a £500 Thousand ($613K USD) Grant to Work on Error Correction

dougfinke — Tue, 28 Jun 2022 04:13:09 +0000

Rigetti Computing and Riverlane have received this grant from Innovate UK, the UK's national innovation agency, to study syndrome extraction on superconducting quantum computers. This would be a critical step for providing error correction on the qubits in a fault tolerant quantum computer. Since a qubit cannot be measured directly without collapsing quantum error correction [...]

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Steven Pinker and I debate AI scaling!

Scott — Tue, 28 Jun 2022 04:00:23 +0000

Before June 2022 was the month of the possible start of the Second American Civil War, it was the month of a lively debate between Scott Alexander and Gary Marcus about the scaling of large language models, such as GPT-3. Will GPT-n be able to do all the intellectual work that humans do, in the limit of large n? If so, should we be impressed? Terrified? Should we dismiss these language models as mere “stochastic parrots”?

I was privileged to be part of various email exchanges about those same questions with Steven Pinker, Ernest Davis, Gary Marcus, Douglas Hofstadter, and Scott Alexander. It’s fair to say that, overall, Pinker, Davis, Marcus, and Hofstadter were more impressed by GPT-3’s blunders, while we Scotts were more impressed by its abilities. (On the other hand, Hofstadter, more so than Pinker, Davis, or Marcus, said that he’s terrified about how powerful GPT-like systems will become in the future.)

Anyway, at some point Steven Pinker produced an essay setting out his thoughts, and asked whether “either of the Scotts” wanted to share it on our blogs. Knowing an intellectual scoop when I see one, I answered that I’d be honored to host Steve’s essay—along with my response, along with Steve’s response to that. To my delight, Steve immediately agreed. Enjoy! –SA

Steven Pinker’s Initial Salvo

Will future deep learning models with more parameters and trained on more examples avoid the silly blunders which Gary Marcus and Ernie Davis entrap GPT into making, and render their criticisms obsolete? And if they keep exposing new blunders in new models, would this just be moving the goalposts? Either way, what’s at stake?

It depends very much on the question. There’s the cognitive science question of whether humans think and speak the way GPT-3 and other deep-learning neural network models do. And there’s the engineering question of whether the way to develop better, humanlike AI is to upscale deep learning models (as opposed to incorporating different mechanisms, like a knowledge database and propositional reasoning).

The questions are, to be sure, related: If a model is incapable of duplicating a human feat like language understanding, it can’t be a good theory of how the human mind works. Conversely, if a model flubs some task that humans can ace, perhaps it’s because it’s missing some mechanism that powers the human mind. Still, they’re not the same question: As with airplanes and other machines, an artificial system can duplicate or exceed a natural one but work in a different way.

Apropos the scientific question, I don’t see the Marcus-Davis challenges as benchmarks or long bets that they have to rest their case on. I see them as scientific probing of an empirical hypothesis, namely whether the human language capacity works like GPT-3. Its failures of common sense are one form of evidence that the answer is “no,” but there are others—for example, that it needs to be trained on half a trillion words, or about 10,000 years of continuous speech, whereas human children get pretty good after 3 years. Conversely, it needs no social and perceptual context to make sense of its training set, whereas children do (hearing children of deaf parents don’t learn spoken language from radio and TV). Another diagnostic is that baby-talk is very different from the output of a partially trained GPT. Also, humans can generalize their language skill to express their intentions across a wide range of social and environmental contexts, whereas GPT-3 is fundamentally a text extrapolator (a task, incidentally, which humans aren’t particularly good at). There are surely other empirical probes, limited only by scientific imagination, and it doesn’t make sense in science to set up a single benchmark for an empirical question once and for all. As we learn more about a phenomenon, and as new theories compete to explain it, we need to develop more sensitive instruments and more clever empirical tests. That’s what I see Marcus and Davis as doing.

Regarding the second, engineering question of whether scaling up deep-learning models will “get us to Artificial General Intelligence”: I think the question is probably ill-conceived, because I think the concept of “general intelligence” is meaningless. (I’m not referring to the psychometric variable g, also called “general intelligence,” namely the principal component of correlated variation across IQ subtests. This is a variable that aggregates many contributors to the brain’s efficiency such as cortical thickness and neural transmission speed, but it is not a mechanism (just as “horsepower” is a meaningful variable, but it doesn’t explain how cars move.) I find most characterizations of AGI to be either circular (such as “smarter than humans in every way,” begging the question of what “smarter” means) or mystical—a kind of omniscient, omnipotent, and clairvoyant power to solve any problem. No logician has ever outlined a normative model of what general intelligence would consist of, and even Turing swapped it out for the problem of fooling an observer, which spawned 70 years of unhelpful reminders of how easy it is to fool an observer.

If we do try to define “intelligence” in terms of mechanism rather than magic, it seems to me it would be something like “the ability to use information to attain a goal in an environment.” (“Use information” is shorthand for performing computations that embody laws that govern the world, namely logic, cause and effect, and statistical regularities. “Attain a goal” is shorthand for optimizing the attainment of multiple goals, since different goals trade off.) Specifying the goal is critical to any definition of intelligence: a given strategy in basketball will be intelligent if you’re trying to win a game and stupid if you’re trying to throw it. So is the environment: a given strategy can be smart under NBA rules and stupid under college rules.

Since a goal itself is neither intelligent or unintelligent (Hume and all that), but must be exogenously built into a system, and since no physical system has clairvoyance for all the laws of the world it inhabits down to the last butterfly wing-flap, this implies that there are as many intelligences as there are goals and environments. There will be no omnipotent superintelligence or wonder algorithm (or singularity or AGI or existential threat or foom), just better and better gadgets.

In the case of humans, natural selection has built in multiple goals—comfort, pleasure, reputation, curiosity, power, status, the well-being of loved ones—which may trade off, and are sometimes randomized or inverted in game-theoretic paradoxical tactics. Not only does all this make psychology hard, but it makes human intelligence a dubious benchmark for artificial systems. Why would anyone want to emulate human intelligence in an artificial system (any more than a mechanical engineer would want to duplicate a human body, with all its fragility)? Why not build the best possible autonomous vehicle, or language translator, or dishwasher-emptier, or baby-sitter, or protein-folding predictor? And who cares whether the best autonomous vehicle driver would be, out of the box, a good baby-sitter? Only someone who thinks that intelligence is some all-powerful elixir.

Back to GPT-3, DALL-E, LaMDA, and other deep learning models: It seems to me that the question of whether or not they’re taking us closer to “Artificial General Intelligence” (or, heaven help us, “sentience”) is based not on any analysis of what AGI would consist of but on our being gobsmacked by what they can do. But refuting our intuitions about what a massively trained, massively parameterized network is capable of (and I’ll admit that they refuted mine) should not be confused with a path toward omniscience and omnipotence. GPT-3 is unquestionably awesome at its designed-in goal of extrapolating text. But that is not the main goal of human language competence, namely expressing and perceiving intentions. Indeed, the program is not even set up to input or output intentions, since that would require deep thought about how to represent intentions, which went out of style in AI as the big-data/deep-learning hammer turned every problem into a nail. That’s why no one is using GPT-3 to answer their email or write an article or legal brief (except to show how well the program can spoof one).

So is Scott Alexander right that every scaled-up GPT-n will avoid the blunders that Marcus and Davis show in GPT-(n-1)? Perhaps, though I doubt it, for reasons that Marcus and Davis explain well (in particular, that astronomical training sets at best compensate for their being crippled by the lack of a world model). But even if they do, that would show neither that human language competence is a GPT (given the totality of the relevant evidence) nor that GPT-n is approaching Artificial General Intelligence (whatever that is).

Scott Aaronson’s Response

As usual, I find Steve crystal-clear and precise—so much so that we can quickly dispense with the many points of agreement. Basically, one side says that, while GPT-3 is of course mind-bogglingly impressive, and while it refuted confident predictions that no such thing would work, in the end it’s just a text-prediction engine that will run with any absurd premise it’s given, and it fails to model the world the way humans do. The other side says that, while GPT-3 is of course just a text-prediction engine that will run with any absurd premise it’s given, and while it fails to model the world the way humans do, in the end it’s mind-bogglingly impressive, and it refuted confident predictions that no such thing would work.

All the same, I do think it’s possible to identify a substantive disagreement between the distinguished baby-boom linguistic thinkers and the gen-X/gen-Y blogging Scott A.’s: namely, whether there’s a coherent concept of “general intelligence.” Steve writes:

No logician has ever outlined a normative model of what general intelligence would consist of, and even Turing swapped it out for the problem of fooling an observer, which spawned 70 years of unhelpful reminders of how easy it is to fool an observer.

I freely admit that I have no principled definition of “general intelligence,” let alone of “superintelligence.” To my mind, though, there’s a simple proof-of-principle that there’s something an AI could do that pretty much any of us would call “superintelligent.” Namely, it could say whatever Albert Einstein would say in a given situation, while thinking a thousand times faster. Feed the AI all the information about physics that the historical Einstein had in 1904, for example, and it would discover special relativity in a few hours, followed by general relativity a few days later. Give the AI a year, and it would think … well, whatever thoughts Einstein would’ve thought, if he’d had a millennium in peak mental condition to think them.

If nothing else, this AI could work by simulating Einstein’s brain neuron-by-neuron—provided we believe in the computational theory of mind, as I’m assuming we do. It’s true that we don’t know the detailed structure of Einstein’s brain in order to simulate it (we might have, had the pathologist who took it from the hospital used cold rather than warm formaldehyde). But that’s irrelevant to the argument. It’s also true that the AI won’t experience the same environment that Einstein would have—so, alright, imagine putting it in a very comfortable simulated study, and letting it interact with the world’s flesh-based physicists. A-Einstein can even propose experiments for the human physicists to do—he’ll just have to wait an excruciatingly long subjective time for their answers. But that’s OK: as an AI, he never gets old.

Next let’s throw into the mix AI Von Neumann, AI Ramanujan, AI Jane Austen, even AI Steven Pinker—all, of course, sped up 1,000x compared to their meat versions, even able to interact with thousands of sped-up copies of themselves and other scientists and artists. Do we agree that these entities quickly become the predominant intellectual force on earth—to the point where there’s little for the original humans left to do but understand and implement the AIs’ outputs (and, of course, eat, drink, and enjoy their lives, assuming the AIs can’t or don’t want to prevent that)? If so, then that seems to suffice to call the AIs “superintelligences.” Yes, of course they’re still limited in their ability to manipulate the physical world. Yes, of course they still don’t optimize arbitrary goals. All the same, these AIs have effects on the real world consistent with the sudden appearance of beings able to run intellectual rings around humans—not exactly as we do around chimpanzees, but not exactly unlike it either.

I should clarify that, in practice, I don’t expect AGI to work by slavishly emulating humans—and not only because of the practical difficulties of scanning brains, especially deceased ones. Like with airplanes, like with existing deep learning, I expect future AIs to take some inspiration from the natural world but also to depart from it whenever convenient. The point is that, since there’s something that would plainly count as “superintelligence,” the question of whether it can be achieved is therefore “merely” an engineering question, not a philosophical one.

Obviously I don’t know the answer to the engineering question: no one does! One could consistently hold that, while the thing I described would clearly count as “superintelligence,” it’s just an amusing fantasy, unlikely to be achieved for millennia if ever. One could hold that all the progress in AI so far, including the scaling of language models, has taken us only 0% or perhaps 0.00001% toward superintelligence so defined.

So let me make two comments about the engineering question. The first is that there’s good news here, at least epistemically: unlike with the philosophical questions, we’re virtually guaranteed more clarity over time! Indeed, we’ll know vastly more just by the end of this decade, as the large language models are further scaled and tweaked, and we find out whether they develop effective representations of the outside world and of themselves, the ability to reject absurd premises and avoid contradicting themselves, or even the ability to generate original mathematical proofs and scientific hypotheses. Of course, Gary Marcus and Scott Alexander have already placed concrete bets on the table for what sorts of things will be possible by 2030. For all their differences in rhetoric, I was struck that their actual probabilities differed much more modestly.

So then what explains the glaring differences in rhetoric? This brings me to my second comment: whenever there’s a new, rapidly-growing, poorly-understood phenomenon, whether it’s the Internet or AI or COVID, there are two wildly different modes of responding to it, which we might call “February 2020 mode” and “March 2020 mode.” In February 2020 mode, one says: yes, a naïve extrapolation might lead someone to the conclusion that this new thing is going to expand exponentially and conquer the world, dramatically changing almost every other domain—but precisely because that conclusion seems absurd on its face, it’s our responsibility as serious intellectuals to articulate what’s wrong with the arguments that lead to it. In March 2020 mode, one says: holy crap, the naïve extrapolation seems right! Prepare!! Why didn’t we start earlier?

Often, to be sure, February 2020 mode is the better mode, at least for outsiders—as with the Y2K bug, or the many disease outbreaks that fizzle. My point here is simply that February 2020 mode and March 2020 mode differ by only a month. Sometimes hearing a single argument, seeing a single example, is enough to trigger an epistemic cascade, causing all the same facts to be seen in a new light. As a result, reasonable people might find themselves on opposite sides of the chasm even if they started just a few steps from each other.

As for me? Well, I’m currently trying to hold the line around February 26, 2020. Suspending my day job in the humdrum, pedestrian field of quantum computing, I’ve decided to spend a year at OpenAI, thinking about the theoretical foundations of AI safety. But for now, only a year.

Steven Pinker’s Response to Scott

Thanks, Scott, for your thoughtful and good-natured reply, and for offering me the opportunity to respond in Shtetl-Optimized, one of my favorite blogs. Despite the areas of agreement, I still think that discussions of AI and its role in human affairs—including AI safety—will be muddled as long as the writers treat intelligence as an undefined superpower rather than a mechanisms with a makeup that determines what it can and can’t do. We won’t get clarity on AI if we treat the “I” as “whatever fools us,” or “whatever amazes us,” or “whatever IQ tests measure,” or “whatever we have more of than animals do,” or “whatever Einstein has more of than we do”—and then start to worry about a superintelligence that has much, much more of whatever that is.

Take Einstein sped up a thousandfold. To begin with, current AI is not even taking us in that direction. As you note, no one is reverse-engineering his connectome, and current AI does not think the way Einstein thought, namely by visualizing physical scenarios and manipulating mathematical equations. Its current pathway would be to train a neural network with billions of physics problems and their solutions and hope that it would soak up the statistical patterns.

Of course, the reason you pointed to a sped-up Einstein was to procrastinate having to define “superintelligence.” But if intelligence is a collection of mechanisms rather than a quantity that Einstein was blessed with a lot of, it’s not clear that just speeding him up would capture what anyone would call superintelligence. After all, in many areas Einstein was no Einstein. You above all could speak of his not-so-superintelligence in quantum physics, and when it came world affairs, in the early 1950s he offered the not exactly prescient or practicable prescription, “Only the creation of a world government can prevent the impending self-destruction of mankind.” So it’s not clear that we would call a system that could dispense such pronouncements in seconds rather than years “superintelligent.” Nor with speeding up other geniuses, say, an AI Bertrand Russell, who would need just nanoseconds to offer his own solution for world peace: the Soviet Union would be given an ultimatum that unless it immediately submitted to world government, the US (which at the time had a nuclear monopoly) would bomb it with nuclear weapons.

My point isn’t to poke retrospective fun at brilliant men, but to reiterate that brilliance itself is not some uncanny across-the-board power that can be “scaled” by speeding it up or otherwise; it’s an engineered system that does particular things in particular ways. Only with a criterion for intelligence can we say which of these counts as intelligent.

Now, it’s true that raw speed makes new kinds of computation possible, and I feel silly writing this to you of all people, but speeding a process up by a constant factor is of limited use with problems that are exponential, as the space of possible scientific theories, relative to their complexity, must be. Speeding up a search in the space of theories a thousandfold would be a rounding error in the time it took to find a correct one. Scientific progress depends on the search exploring the infinitesimal fraction of the space in which the true theories are likely to lie, and this depends on the quality of the intelligence, not just its raw speed.

And it depends as well on a phenomenon you note, namely that scientific progress depends on empirica

Quantum network between two national labs achieves record synch

lisar — Mon, 27 Jun 2022 14:00:09 +0000

Editor’s note: Below is a press release jointly issued by Fermilab and Argonne National Laboratory.

The world awaits quantum technology. Quantum computing is expected to solve complex problems that current, or classical, computing cannot. And quantum networking is essential for realizing the full potential of quantum computing, enabling breakthroughs in our understanding of nature, as well as applications that improve everyday life.

But making it a reality requires the development of precise quantum computers and reliable quantum networks that leverage current computer technologies and existing infrastructure.

Recently, as a sort of proof of potential and a first step toward functional quantum networks, a team of researchers with the Illinois‐Express Quantum Network (IEQNET) successfully deployed a long-distance quantum network between two U.S. Department of Energy (DOE) laboratories using local fiber optics.

The experiment marked the first time that quantum-encoded photons — the particle through which quantum information is delivered — and classical signals were simultaneously delivered across a metropolitan-scale distance with an unprecedented level of synchronization.

The IEQNET collaboration includes the DOE’s Fermi National Accelerator and Argonne National laboratories, Northwestern University and Caltech. Their success is derived, in part, from the fact that its members encompass the breadth of computing architectures, from classical and quantum to hybrid.

“To have two national labs that are 50 kilometers apart, working on quantum networks with this shared range of technical capability and expertise, is not a trivial thing,” said Panagiotis Spentzouris, head of the Quantum Science Program at Fermilab and lead researcher on the project. “You need a diverse team to attack this very difficult and complex problem.”

To test the synchronicity of two clocks — one at Argonne and one at Fermilab — scientists transmitted a traditional clock signal (blue) and a quantum signal (orange) simultaneously between the two clocks. The signals were sent over the Illinois Express Quantum Network. Researchers found that the two clocks remained synchronized within a time window smaller than 5 picoseconds, or 5 trillionths of a second. Image: Lee Turman, Argonne

And for that team, synchronization proved the beast to tame. Together, they showed that it is possible for quantum and classical signals to coexist across the same network fiber and achieve synchronization, both in metropolitan-scale distances and real-world conditions.

Classical computing networks, the researchers point out, are complex enough. Introducing the challenge that is quantum networking into the mix changes the game considerably.

When classical computers need to execute synchronized operations and functions, like those required for security and computation acceleration, they rely on something called the Network Time Protocol. This protocol distributes a clock signal over the same network that carries information, with a precision that is a million times faster than a blink of an eye.

With quantum computing, the precision required is even greater. Imagine that the classical network time protocol is an Olympic runner; the clock for quantum computing is The Flash, the superfast superhero from comic books and films.

To assure that they get pairs of photons that are entangled — the ability to influence one another from a distance — the researchers must generate the quantum-encoded photons in great numbers.

Knowing which pairs are entangled is where the synchronicity comes in. The team used similar timing signals to synchronize the clocks at each destination, or node, across the Fermilab-Argonne network.

“To have two national labs that are 50 kilometers apart, working on quantum networks with this shared range of technical capability and expertise, is not a trivial thing.” Panagiotis Spentzouris, head of the Quantum Science Program at Fermilab

Precision electronics are used to adjust this timing signal based on known factors, like distance and speed — in this case, that photons always travel at the speed of light — as well as for interference generated by the environment, such as temperature changes or vibrations, in the fiber optics.

Because they had only two fiber strands between the two labs, the researchers had to send the clock on the same fiber that carried the entangled photons. The way to separate the clock from the quantum signal is to use different wavelengths, but that comes with its own challenge.

“Choosing appropriate wavelengths for the quantum and classical synchronization signals is very important for minimizing interference that will affect the quantum information,” said Rajkumar Kettimuthu, an Argonne computer scientist and project team member. “One analogy could be that the fiber is a road, and wavelengths are lanes. The photon is a cyclist, and the clock is a truck. If we are not careful, the truck can cross into the bike lane. So, we performed a large number of experiments to make sure the truck stayed in its lane.”

Ultimately, the two were properly assigned and controlled, and the timing signal and photons were distributed from sources at Fermilab. As the photons arrived at each location, measurements were performed and recorded using Argonne’s superconducting nanowire single photon detectors.

“We showed record levels of synchronization using readily available technology that relies on radio frequency signals encoded onto light,” said Raju Valivarthi, a Caltech researcher and IEQNET team member. “We built and tested the system at Caltech, and the IEQNET experiments demonstrate its readiness and capabilities in a real-world fiber optic network connecting two major national labs.”

The network was synchronized so accurately that it recorded only a five-picosecond time difference in the clocks at each location; one picosecond is one trillionth of a second.

Such precision will allow scientists to accurately identify and manipulate entangled photon pairs for supporting quantum network operations over metropolitan distances in real-world conditions. Building on this accomplishment, the IEQNET team is getting ready to perform experiments to demonstrate entanglement swapping. This process enables entanglement between photons from different entangled pairs, thus creating longer quantum communication channels.

“This is the first demonstration in real conditions to use real optical fiber to achieve this type of superior synchronization accuracy and the ability to coexist with quantum information,” Spentzouris said. “This record performance is an essential step on the path to building practical multinode quantum networks.”

This project was funded through the DOE Office of Science, Advanced Scientific Computing Research program.

Fermi National Accelerator Laboratory is America’s premier national laboratory for particle physics research. A U.S. Department of Energy Office of Science laboratory, Fermilab is located near Chicago, Illinois, and operated under contract by the Fermi Research Alliance LLC. Visit Fermilab’s website at https://www.fnal.gov and follow us on Twitter @Fermilab.

The DOE Office of Science is the single largest supporter of basic research in the physical sciences in the United States and is working to address some of the most pressing challenges of our time. For more information, please visit science.energy.gov.

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Because I couldn’t not post

Scott — Fri, 24 Jun 2022 15:31:38 +0000

In 1973, the US Supreme Court enshrined the right to abortion—considered by me and ~95% of everyone I know to be a basic pillar of modernity—in such a way that the right could be overturned only if its opponents could somehow gain permanent minority rule, and thereby disregard the wills of three-quarters of Americans. So now, half a century later, that’s precisely what they’ve done. Because Ruth Bader Ginsburg didn’t live three more weeks, we’re now faced with a civilizational crisis, with tens of millions of liberals and moderates in the red states now under the authority of a social contract that they never signed. With this backwards leap, Curtis Yarvin’s notion that “Cthulhu only ever swims leftward” stands as decimated by events as any thesis has ever been. I wonder whether Yarvin is happy to have been so thoroughly refuted.

Most obviously for me, the continued viability of Texas as a place for science, for research, for technology companies, is now in severe doubt. Already this year, our 50-member CS department at UT Austin has had faculty members leave, and faculty candidates turn us down, with abortion being the stated reason, and I expect that to accelerate. Just last night my wife, Dana Moshkovitz, presented a proposal at the STOC business meeting to host STOC’2024 at a beautiful family-friendly resort outside Austin. The proposal failed, in part because of the argument that, if a pregnant STOC attendee faced a life-threatening medical condition, Texas doctors might choose to let her die, or the attendee might be charged with murder for having a miscarriage. In other words: Texas (and indeed, half the US) will apparently soon be like Donetsk or North Korea, dangerous for Blue Americans to visit even for just a few days. To my fellow Texans, I say: if you find that hyperbolic, understand that this is how the blue part of the country now sees you. Understand that only a restoration of the previous social compact can reverse it.

Of course, this destruction of everything some of us have tried to build in science in Texas is happening despite the fact that 47-48% of Texans actually vote Democratic. It’s happening despite the fact that, if Blue Americans wanted to stop it, the obvious way to do so would be to move to Austin and Houston (and the other blue enclaves of red states) in droves, and exert their electoral power. In other words, to do precisely what Dana and I did. But can I urge others to do the same with a straight face?

As far as I can tell, the only hope at this point of averting a cold Civil War is if, against all odds, there’s a Democratic landslide in Congress, sufficient to get the right to abortion enshrined into federal law. Given the ways both the House and the Senate are stacked against Democrats, I don’t expect that anytime soon, but I’ll work for it—and will do so even if many of the people I’m working with me despise me for other reasons. I will match reader donations to Democratic PACs and Congressional campaigns (not necessarily the same ones, though feel free to advocate for your favorites), announced in the comment section of this post, up to a limit of $10,000.

Quantum network nodes with warm atoms

Fri, 24 Jun 2022 14:51:36 +0000

Communication networks need nodes at which information is processed or rerouted. Physicists have now developed a network node for quantum communication networks that can store single photons in a vapor cell and pass them on later.

Topological superconductors: Fertile ground for elusive Majorana ('angel') particle

Wed, 22 Jun 2022 14:13:42 +0000

A new review investigates the search of Majorana fermions in iron-based superconductors. The elusive Majorana fermion, or 'angel particle' simultaneously behaves like a particle and an antiparticle -- and surprisingly remains stable rather than being self-destructive. Majorana fermions promise information and communications technology with zero resistance, addressing the rising energy consumption of modern electronics (already 8% of global electricity consumption), promising a sustainable future for computing. Majorana zero-energy modes in topological superconductors makes those exotic quantum materials the main candidate materials for realizing topological quantum computing.

Nanostructured surfaces for future quantum computer chips

Wed, 22 Jun 2022 14:13:29 +0000

Quantum computers are one of the key future technologies of the 21st century. Researchers have developed a new technology for manipulating light that can be used as a basis for future optical quantum computers.

Rigetti Switches on their UK Quantum Computer

dougfinke — Wed, 22 Jun 2022 03:32:43 +0000

In a project funded by the UK government’s Quantum Technologies Challenge led by UK Research & Innovation, Rigetti has launched its a 32-qubit Aspen-series system and has made it accessible to Rigetti's UK partners over the cloud through the Rigetti QCS™ cloud platform. This project was first announced in September 2020 with £10M ($13.35M USD) in [...]

The post Rigetti Switches on their UK Quantum Computer appeared first on Quantum Computing Report.

Quantum sensor can detect electromagnetic signals of any frequency

Tue, 21 Jun 2022 22:44:58 +0000

Researchers developed a method to enable quantum sensors to detect any arbitrary frequency, with no loss of their ability to measure nanometer-scale features. Quantum sensors detect the most minute variations in magnetic or electrical fields, but until now they have only been capable of detecting a few specific frequencies, limiting their usefulness.

Keysight Introduces a New Quantum Control System for Use in Quantum Processors

dougfinke — Tue, 21 Jun 2022 19:34:18 +0000

Keysight Quantum Control System. Credit: Keysight One of the most important elements in designing superconducting or spin qubit based quantum processors is the control electronics that provide the needed controls for operating the qubits. Qubits are controlled by precise microwave signals that will determine what gate operations are performed in accordance with the desired software [...]

The post Keysight Introduces a New Quantum Control System for Use in Quantum Processors appeared first on Quantum Computing Report.

EvolutionQ receives $7 million in funding

25267 — Mon, 20 Jun 2022 00:00:00 +0000

Monday, June 20, 2022

EvolutionQ, founded by Norbert Lütkenhaus, Executive Director of the Institute for Quantum Computing, and IQC faculty member Michele Mosca, has secured $7 million in funding for quantum-safe cybersecurity. EvolutionQ is looking to help organizations prepare themselves for quantum computers. Their Series A financing is led by Quantonation, a Paris-based, quantum technology-focused VC fund, with support from Toronto’s The Group Ventures, to “scale up” its quantum-safe cybersecurity tech.

OpenAI!

Scott — Fri, 17 Jun 2022 23:39:17 +0000

I have some exciting news (for me, anyway). Starting next week, I’ll be going on leave from UT Austin for one year,to work at OpenAI. They’re the creators of the astonishing GPT-3 and DALL-E2, which have not only endlessly entertained me and my kids, but recalibrated my understanding of what, for better and worse, the world is going to look like for the rest of our lives. Working with an amazing team at OpenAI, including Jan Leike, John Schulman, and Ilya Sutskever, my job will be think about the theoretical foundations of AI safety and alignment. What, if anything, can computational complexity contribute to a principled understanding of how to get an AI to do what we want and not do what we don’t want?

Yeah, I don’t know the answer either. That’s why I’ve got a whole year to try to figure it out! One thing I know for sure, though, is that I’m interested both in the short-term, where new ideas are now quickly testable, and where the misuse of AI for spambots, surveillance, propaganda, and other nefarious purposes is already a major societal concern, and the long-term, where one might worry about what happens once AIs surpass human abilities across nearly every domain. (And all the points in between: we might be in for a long, wild ride.) When you start reading about AI safety, it’s striking how there are two separate communities—the one mostly worried about machine learning perpetuating racial and gender biases, and the one mostly worried about superhuman AI turning the planet into goo—who not only don’t work together, but are at each other’s throats, with each accusing the other of totally missing the point. I persist, however, in the possibly-naïve belief that these are merely two extremes along a single continuum of AI worries. By figuring out how to align AI with human values today—constantly confronting our theoretical ideas with reality—we can develop knowledge that will give us a better shot at aligning it with human values tomorrow.

For family reasons, I’ll be doing this work mostly from home, in Texas, though traveling from time to time to OpenAI’s office in San Francisco. I’ll also spend 30% of my time continuing to run the Quantum Information Center at UT Austin and working with my students and postdocs. At the end of the year, I plan to go back to full-time teaching, writing, and thinking about quantum stuff, which remains my main intellectual love in life, even as AI—the field where I started, as a PhD student, before I switched to quantum computing—has been taking over the world in ways that none of us can ignore.

Maybe fittingly, this new direction in my career had its origins here on Shtetl-Optimized. Several commenters, including Max Ra and Matt Putz, asked me point-blank what it would take to induce me to work on AI alignment. Treating it as an amusing hypothetical, I replied that it wasn’t mostly about money for me, and that:

The central thing would be finding an actual potentially-answerable technical question around AI alignment, even just a small one, that piqued my interest and that I felt like I had an unusual angle on. In general, I have an absolutely terrible track record at working on topics because I abstractly feel like I “should” work on them. My entire scientific career has basically just been letting myself get nerd-sniped by one puzzle after the next.

Anyway, Jan Leike at OpenAI saw this exchange and wrote to ask whether I was serious in my interest. Oh shoot! Was I? After intensive conversations with Jan, others at OpenAI, and others in the broader AI safety world, I finally concluded that I was.

I’ve obviously got my work cut out for me, just to catch up to what’s already been done in the field. I’ve actually been in the Bay Area all week, meeting with numerous AI safety people (and, of course, complexity and quantum people), carrying a stack of technical papers on AI safety everywhere I go. I’ve been struck by how, when I talk to AI safety experts, they’re not only not dismissive about the potential relevance of complexity theory, they’re more gung-ho about it than I am! They want to talk about whether, say, IP=PSPACE, or MIP=NEXP, or the PCP theorem could provide key insights about how we could verify the behavior of a powerful AI. (Short answer: maybe, on some level! But, err, more work would need to be done.)

How did this complexitophilic state of affairs come about? That brings me to another wrinkle in the story. Traditionally, students follow in the footsteps of their professors. But in trying to bring complexity theory into AI safety, I’m actually following in the footsteps of my student: Paul Christiano, one of the greatest undergrads I worked with in my nine years at MIT, the student whose course project turned into the Aaronson-Christiano quantum money paper. After MIT, Paul did a PhD in quantum computing at Berkeley, with my own former adviser Umesh Vazirani, while also working part-time on AI safety. Paul then left quantum computing to work on AI safety full-time—indeed, he founded the safety group at OpenAI. Paul has since left to found his own AI safety organization, the Alignment Research Center (ARC), although he remains on good terms with the OpenAI folks. Paul is largely responsible for bringing complexity theory intuitions and analogies into AI safety—for example, through the “AI safety via debate” paper and the Iterated Amplification paper. I’m grateful for Paul’s guidance and encouragement—as well as that of the others now working in this intersection, like Geoffrey Irving and Elizabeth Barnes—as I start this new chapter.

So, what projects will I actually work on at OpenAI? Yeah, I’ve been spending the past week trying to figure that out. I still don’t know, but a few possibilities have emerged. First, I might work out a general theory of sample complexity and so forth for learning in dangerous environments—i.e., learning where making the wrong query might kill you. Second, I might work on explainability and interpretability for machine learning: given a deep network that produced a particular output, what do we even mean by an “explanation” for “why” it produced that output? What can we say about the computational complexity of finding that explanation? Third, I might work on the ability of weaker agents to verify the behavior of stronger ones. Of course, if P≠NP, then the gap between the difficulty of solving a problem and the difficulty of recognizing a solution can sometimes be enormous. And indeed, even in empirical machine learing, there’s typically a gap between the difficulty of generating objects (say, cat pictures) and the difficulty of discriminating between them and other objects, the latter being easier. But this gap typically isn’t exponential, as is conjectured for NP-complete problems: it’s much smaller than that. And counterintuitively, we can then turn around and use the generators to improve the discriminators. How can we understand this abstractly? Are there model scenarios in complexity theory where we can prove that something similar happens? How far can we amplify the generator/discriminator gap—for example, by using interactive protocols, or debates between competing AIs?

OpenAI, of course, has the word “open” right in its name, and a founding mission “to ensure that artificial general intelligence benefits all of humanity.” But it’s also a for-profit enterprise, with investors and paying customers and serious competitors. So throughout the year, don’t expect me to share any proprietary information—that’s not my interest anyway, even if I hadn’t signed an NDA. But do expect me to blog my general thoughts about AI safety as they develop, and to solicit feedback from readers.

In the past, I’ve often been skeptical about the prospects for superintelligent AI becoming self-aware and destroying the world anytime soon (see, for example, my 2008 post The Singularity Is Far). While I was aware since 2005 or so of the AI-risk community; and of its leader and prophet, Eliezer Yudkowsky; and of Eliezer’s exhortations for people to drop everything else they’re doing and work on AI risk, as the biggest issue facing humanity, I … kept the whole thing at arms’ length. Even supposing I agreed that this was a huge thing to worry about, I asked, what on earth do you want me to do about it today? We know so little about a future superintelligent AI and how it would behave that any actions we took today would likely be useless or counterproductive.

Over the past 15 years, though, my and Eliezer’s views underwent a dramatic and ironic reversal. If you read Eliezer’s “litany of doom” from two weeks ago, you’ll see that he’s now resigned and fatalistic: because his early warnings weren’t heeded, he argues, humanity is almost certainly doomed and an unaligned AI will soon destroy the world. He says that there are basically no promising directions in AI safety research: for any alignment strategy anyone points out, Eliezer can trivially refute it by explaining how (e.g.) the AI would be wise to the plan, and would pretend to go along with whatever we wanted from it while secretly plotting against us.

The weird part is, just as Eliezer became more and more pessimistic about the prospects for getting anywhere on AI alignment, I’ve become more and more optimistic. Part of my optimism is because people like Paul Christiano have laid foundations for a meaty mathematical theory: much like the Web (or quantum computing theory) in 1992, it’s still in a ridiculously primitive stage, but even my limited imagination now suffices to see how much more could be built there. An even greater part of my optimism is because we now live in a world with GPT-3, DALL-E2, and other systems that, while they clearly aren’t AGIs, are powerful enough that worrying about AGIs has come to seem more like prudence than like science fiction. I didn’t predict that such things would be possible by 2022. Most of you probably didn’t predict it. For godsakes, Eliezer Yudkowsky didn’t predict it. But it’s happened. And to my mind, one of the defining virtues of science is that, when empirical reality gives you a clear shock, you update and adapt, rather than expending your intelligence to come up with clever reasons why it doesn’t matter or doesn’t count.

Anyway, so that’s the plan! If I can figure out a way to save the galaxy, I will, but I’ve set my goals slightly lower, at learning some new things and doing some interesting research and writing some papers about it and enjoying a break from teaching. Wish me a non-negligible success probability!

IQC student recognized with W.B. Pearson Medal for creative research

51 — Fri, 17 Jun 2022 00:00:00 +0000

Friday, June 17, 2022

Jie Lin, PhD candidate in the Department of Physics and Astronomy and the Institute for Quantum Computing (IQC), earned the W.B. Pearson Medal for his PhD thesis Security Analysis of Quantum Key Distribution: Methods and Applications.

Quantum simulator delivers new insight

Thu, 16 Jun 2022 18:27:38 +0000

A quantum simulator is giving physicists a clear look at spin-charge separation, a bizarre phenomenon in which two parts of indivisible particles called electrons travel at different speeds in extremely cold 1D wires. The research has implications for quantum computing and electronics with atom-scale wires.

Diamonds are for quantum sensing

Thu, 16 Jun 2022 16:16:33 +0000

Researchers measured tiny magnetic fields with unprecedented speed. By monitoring spins at nitrogen-vacancy centers along using ultrafast spectroscopy, this work may lead to extremely accurate future quantum computers.

What quantum information and snowflakes have in common, and what we can do about it

Wed, 15 Jun 2022 15:32:23 +0000

Qubits, the basic building blocks of quantum computers, are as fragile as snowflakes. Now, researchers have come up with a new way of reading out the information from certain kinds of qubits without destroying them in the process, potentially paving the way for a quantum internet.

Quantum computer programming basics

Tue, 14 Jun 2022 22:41:03 +0000

For would-be quantum programmers scratching their heads over how to jump into the game as quantum computers proliferate and become publicly accessible, a new beginner's guide provides a thorough introduction to quantum algorithms and their implementation on existing hardware. Deep-diving guide explains the basics, surveys major quantum algorithms and steps through implementing them on publicly available quantum computers.

Calculating the 'fingerprints' of molecules with artificial intelligence

Tue, 14 Jun 2022 13:56:34 +0000

With conventional methods, it is extremely time-consuming to calculate the spectral fingerprint of larger molecules. But this is a prerequisite for correctly interpreting experimentally obtained data. Now, a team has achieved very good results in significantly less time using self-learning graphical neural networks.

The potential of probabilistic computers

Mon, 13 Jun 2022 23:34:53 +0000

The rise of artificial intelligence (AI) and machine learning (ML) has created a crisis in computing and a significant need for more hardware that is both energy-efficient and scalable. A key step in both AI and ML is making decisions based on incomplete data, the best approach for which is to output a probability for each possible answer. Current classical computers are not able to do that in an energy-efficient way, a limitation that has led to a search for novel approaches to computing. Quantum computers, which operate on qubits, may help meet these challenges, but they are extremely sensitive to their surroundings, must be kept at extremely low temperatures and are still in the early stages of development.

Alright, so here are my comments…

Scott — Sun, 12 Jun 2022 17:56:29 +0000

… on Blake Lemoine, the Google engineer who became convinced that a machine learning model had become sentient, contacted federal government agencies about it, and was then ~~fired~~ placed on administrative leave for violating Google’s confidentiality policies.

(1) I don’t think Lemoine is right that LaMDA is at all sentient, but the transcript is so mind-bogglingly impressive that I did have to stop and think for a second! Certainly, if you sent the transcript back in time to 1990 or whenever, even an expert reading it might say, yeah, it looks like by 2022 AGI has more likely been achieved than not (“but can I run my own tests?”). Read it for yourself, if you haven’t yet.

(2) Reading Lemoine’s blog and Twitter this morning, he holds many views that I disagree with, not just about the sentience of LaMDA. Yet I’m touched and impressed by how principled he is, and I expect I’d hit it off with him if I met him. I wish that Google wouldn’t fire him.

Computer scientists crash the Solvay Conference

Scott — Thu, 09 Jun 2022 20:43:19 +0000

Thanks so much to everyone who sent messages of support following my last post! I vowed there that I’m going to stop letting online trolls and sneerers occupy so much space in my mental world. Truthfully, though, while there are many trolls and sneerers who terrify me, there are also some who merely amuse me. A good example of the latter came a few weeks ago, when an anonymous commenter calling themselves “String Theorist” submitted the following:

It’s honestly funny to me when you [Scott] call yourself a “nerd” or a “prodigy” or whatever [I don’t recall ever calling myself a “prodigy,” which would indeed be cringe, though “nerd” certainly —SA], as if studying quantum computing, which is essentially nothing more than glorified linear algebra, is such an advanced intellectual achievement. For what it’s worth I’m a theoretical physicist, I’m in a completely different field, and I was still able to learn Shor’s algorithm in about half an hour, that’s how easy this stuff is. I took a look at some of your papers on arXiv and the math really doesn’t get any more advanced than linear algebra. To understand quantum circuits about the most advanced concept is a tensor product which is routinely covered in undergraduate linear algebra. Wheras in my field of string theory grasping, for instance, holographic dualities relating confirmal field theories and gravity requires vastly more expertise (years of advanced study). I actually find it pretty entertaining that you’ve said yourself you’re still struggling to understand QFT, which most people I’m working with in my research group were first exposed to in undergrad Christine Muschik was named a 2022 University Research Chair at the last University Senate meeting.

The University Research Chair award recognizes exceptional achievement and pre-eminence in a particular field of knowledge. Recipients can choose to receive an annual stipend or teaching reduction of one course per year.

Mining valuable insights from diamonds

Poornima Apte | Department of Nuclear Science and Engineering — Tue, 31 May 2022 04:00:00 +0000

If Changhao Li were to trace the origins of his love of nature, he would point to the time when he was 9, observing the night sky from his childhood home in the small town of Jinan, China. “At that moment I felt that nature is so beautiful, I just wanted to go outside the Earth, to go to the moon or even Mars,” Li remembers.

That childhood dream seeded his love of physics, which he pursued through middle and high school, and eventually at Xi’an Jiaotong University in China.

Li’s passion for the skies has since taken a more earthbound and microscopic form: It has translated into a love of quantum physics. Li is a fifth-year doctoral candidate in the Department of Nuclear Science and Engineering (NSE) and researches quantum information science, including quantum sensing and computation, with Professor Paola Cappellaro.

Quantum leaps

The primary thesis driving quantum information science is that altering the state of a material at a subatomic level can make a significant impact at much larger scales. Quantum computing, for example, depends on the most minute changes in material properties to store and process more information than a simple classical binary mode could.

The basic unit of information in quantum computing, equivalent to a bit in classical computers, is called a qubit. Exploiting defects in material structures is one way to manufacture these qubits.

An aspect of Li’s research focuses on defects in very small diamonds, some of which are on the nanometer scale. Experiments involve introducing an atomic-scale defect, known as nitrogen vacancy centers, in these diamonds, and subjecting the defects to extremely minute perturbations, using microwaves or lasers, to create and control quantum states.

One of Li’s projects measures the fluorescence emitted by a disturbed diamond to give us more information about the external stimulus. Just like you would measure an oven’s temperature to gauge how hot it is, measuring the fluorescence emitted by such a defective diamond can tell us what it is sensing and by how much. For example, a sensor that could detect even a few hundreds of strands of the SARS-CoV-2 virus that causes Covid-19 is one of the applications that Li is exploring with his colleagues.

In Physical Review Letters, Li has published findings from another research project which evaluates the symmetry of quantum systems. To explore the properties of quantum systems, we need to understand how the quantum states behave over time, and their symmetries are important. “Engineering a system with desired symmetry is a nontrivial task,” Li says. “Quantum properties are very unstable because they can interact with the environment. We need a very good lifetime for our qubits, and here we developed a method to control and characterize such a system.” Yet another research focus, the findings from which are soon to be published, focuses on simulation of a tensor gauge field using defects in diamonds, which is related with fundamental science.

Li says understanding quantum information is primarily about studying basic science. “The basic principles of this world are beautiful and can explain many interesting phenomena,” he points out. “This allows me to explore the universe, to understand how nature works,” Li adds.

The man who travels far knows more

A passion to understand how nature works, whether at the scale of stars or a small quantum unit, has galvanized Li’s interest in physics ever since he was a boy.

His parents encouraged his love of physics and a middle school teacher taught him to think critically, to spot errors in his textbooks and not swallow information as truths. “You need to run simple experiments to find the truth for yourself,” was the lesson that Li took away from middle school.

With that lesson safely tucked away, Li found high school to be a little more challenging and initially placed toward the middle of the nearly 1,000 students. But hard work and learning from others steered him toward the top.

Placing top of his class in both middle and high school, Li went on to pursue physics at Xi’an Jiaotong University, about 600 miles west of Beijing. It was his first time away from home, and he found his schooling needed a boost in topics like linear algebra. Again, hard work paid off and Li graduated top of his class.

University gave Li the ability to study in the United States for parts of his sophomore and junior years of college. Through exchange programs, Li attended University of Notre Dame for two months of summer research in 2015 and attended the University of California at Berkeley, during 2016, his junior year. The trips reinforced one of Li’s favorite quotes: “The man who travels far, knows more.”

Notre Dame was Li’s first time abroad — he remembers trying to get used to hamburgers and fries, a radical departure from the traditional Chinese food he loves.

It was research at Berkeley — he remembers the university library and dining halls fondly — that cemented his love of quantum physics. By the time he returned to China he knew he wanted to attend graduate school and pursue research in the field. MIT’s NSE beckoned as a chance to “work with the most brilliant people in the world,” Li says. Cappellaro is his inspiration — “she taught me how to think about research, I am very grateful,” he says.

Spare time finds Li relearning Chinese cooking — his parents do help with tips — and playing mobile games such as “Arena of Valor” with friends. Learning to play the guitar has been his pandemic hobby.

His fundamental love of nature, and in learning how things work, continues to inspire Li. “I’m focusing on the tiniest of things and there’s something really wonderful there. It’s about nature too, right? If you learn about how this tiny thing works, you can maybe also learn how the bigger things work,” Li says.

University Research Chair awarded to William Slofstra

25267 — Tue, 31 May 2022 00:00:00 +0000

Tuesday, May 31, 2022

An IQC faculty member and researcher in the Department of Pure Mathematics is among the latest winners of a University Research Chair.

William Slofstra, an assistant professor of pure mathematics and a faculty member with the Institute for Quantum Computing, works in the field of mathematics of quantum information and computation.

An understandable failing?

Scott — Sun, 29 May 2022 18:44:25 +0000

I hereby precommit that this will be my last post, for a long time, around the twin themes of (1) the horribleness in the United States and the world, and (2) my desperate attempts to reason with various online commenters who hold me personally complicit in all this horribleness. I should really focus my creativity more on actually fixing the world’s horribleness, than on seeking out every random social-media mudslinger who blames me for it, shouldn’t I? Still, though, isn’t undue obsession with the latter a pretty ordinary human failing, a pretty understandable one?

So anyway, if you’re one of the thousands of readers who come here simply to learn more about quantum computing and computational complexity, rather than to try to provoke me into mounting a public defense of my own existence (which defense will then, ironically but inevitably, stimulate even more attacks that need to be defended against) … well, either scroll down to the very end of this post, or wait for the next post.

Thanks so much to all my readers who donated to Fund Texas Choice. As promised, I’ve personally given them a total of $4,106.28, to match the donations that came in by the deadline. I’d encourage people to continue donating anyway, while for my part I’ll probably run some more charity matching campaigns soon. These things are addictive, like pulling the lever of a slot machine, but where the rewards go to making the world an infinitesimal amount more consistent with your values.

Of course, now there’s a brand-new atrocity to shame my adopted state of Texas before the world. While the Texas government will go to extraordinary lengths to protect unborn children, the world has now witnessed 19 of itsborn children consigned to gruesome deaths, as the “good guys with guns”—waited outside and prevented parents from entering the classrooms where their children were being shot. I have nothing original to add to the global outpourings of rage and grief. Forget about the statistical frequency of these events: I know perfectly well that the risk from car crashes and home accidents is orders-of-magnitude greater. Think about it this way: the United States is now known to the world as “the country that can’t or won’t do anything to stop its children from semi-regularly being gunned down in classrooms,” not even measures that virtually every other comparable country on earth has successfully taken. It’s become the symbol of national decline, dysfunction, and failure. If so, then the stakes here could fairly be called existential ones—not because of its direct effects on child life expectancy or GDP or any other index of collective well-being that you can define and measure, but rather, because a country that lacks the will to solve this will be judged by the world, and probably accurately, as lacking the will to solve anything else.

In return for the untold thousands of hours I’ve poured into this blog, which has never once had advertising or asked for subscriptions, my reward has been years of vilification by sneerers and trolls. Some of the haters even compare me to Elliot Rodger and other aggrieved mass shooters. And I mean: yes, it’s true that I was bullied and miserable for years. It’s true that Elliot Rodger, Salvador Ramos (the Uvalde shooter), and most other mass shooters were also bullied and miserable for years. But, Scott-haters, if we’re being intellectually honest about this, we might say that the similarities between the mass shooter story and the Scott Aaronson story end at a certain point not very long after that. We might say: it’s not just that Aaronson didn’t respond by hurting anybody—rather, it’s that his response loudly affirmed the values of the Enlightenment, meaning like, the whole package, from individual autonomy to science and reason to the rejection of sexism and racism to everything in between. Affirmed it in a manner that’s not secretly about popularity (demonstrably so, because it doesn’t get popularity), affirmed it via self-questioning methods intellectually honest enough that they’d probably still have converged on the right answer even in situations where it’s now obvious that almost everyone you around would’ve been converging on the wrong answer, like (say) Nazi Germany or the antebellum South.

I’ve been to the valley of darkness. While there, I decided that the only “revenge” against the bullies that was possible or desirable was to do something with my life, to achieve something in science that at least some bullies might envy, while also starting a loving family and giving more than most to help strangers on the Internet and whatever good cause comes to his attention and so on. And after 25 years of effort, some people might say I’ve sort of achieved the “revenge” as I’d then defined it. And they might further say: if you could get every school shooter to redefine “revenge” as “becoming another Scott Aaronson,” that would be, you know, like, a step upwards. An improvement.

And let this be the final word on the matter that I ever utter in all my days, to the thousands of SneerClubbers and Twitter randos who pursue this particular line of attack against Scott Aaronson (yes, we do mean the thousands—which means, it both feels to its recipient like the entire earth yet actually is less than 0.01% of the earth).

We see what Scott did with his life, when subjected for a decade to forms of psychological pressure that are infamous for causing young males to lash out violently. What would you have done with your life?

A couple weeks ago, when the trolling attacks were arriving minute by minute, I toyed with the idea of permanently shutting down this blog. What’s the point? I asked myself. Back in 2005, the open Internet was fun; now it’s a charred battle zone. Why not restrict conversation to my academic colleagues and friends? Haven’t I done enough for a public that gives me so much grief? I was dissuaded by many messages of support from loyal readers. Thank you so much.

If anyone needs something to cheer them up, you should really watch Prehistoric Planet, narrated by an excellent, 96-year-old David Attenborough. Maybe 35 years from now, people will believe dinosaurs looked or acted somewhat differently from these portrayals, just like they believe somewhat differently now from when I was a kid. On the other hand, if you literally took a time machine to the Late Cretaceous and starting filming, you couldn’t get a result that seemed more realistic, let’s say to a documentary-watching child, than these CGI dinosaurs on their CGI planet seem. So, in the sense of passing that child’s Turing Test, you might argue, the problem of bringing back the dinosaurs has now been solved.

If you … err … really want to be cheered up, you can follow up with Dinosaur Apocalypse, also narrated by Attenborough, where you can (again, as if you were there) watch the dinosaurs being drowned and burned alive in their billions when the asteroid hits. We’d still be scurrying under rocks, were it not for that lucky event that only a monster could’ve called lucky at the time.

Several people asked me to comment on the recent savage investor review against the quantum computing startup IonQ. The review amusingly mixed together every imaginable line of criticism, with every imaginable degree of reasonableness from 0% to 100%. Like, quantum computing is impossible even in theory, and (in the very next sentence) other companies are much closer to realizing quantum computing than IonQ is. And IonQ’s response to the criticism, and see also this by the indefatigable Gil Kalai.

Is it, err, OK if I sit this one out for now? There’s probably, like, actually an already-existing machine learning model where, if you trained it on all of my previous quantum computing posts, it would know exactly what to say about this.

Researchers teleport quantum information across rudimentary quantum network

Wed, 25 May 2022 17:11:56 +0000

Researchers have succeeded in teleporting quantum information across a rudimentary network. This first of its kind is an important step towards a future quantum Internet. This breakthrough was made possible by a greatly improved quantum memory and enhanced quality of the quantum links between the three nodes of the network.

Toward error-free quantum computing

Wed, 25 May 2022 15:08:52 +0000

For quantum computers to be useful in practice, errors must be detected and corrected. A team of experimental physicists has now implemented a universal set of computational operations on fault-tolerant quantum bits for the first time, demonstrating how an algorithm can be programmed on a quantum computer so that errors do not spoil the result.

Secure communication with light particles

Wed, 25 May 2022 14:29:40 +0000

While quantum computers offer many novel possibilities, they also pose a threat to internet security since these supercomputers make common encryption methods vulnerable. Based on the so-called quantum key distribution, researchers have developed a new, tap-proof communication network.

Breakthrough in quantum universal gate sets: A high-fidelity iToffoli gate

Tue, 24 May 2022 16:49:16 +0000

Researchers have demonstrated the first three-qubit high-fidelity iToffoli native gate in a superconducting quantum information processor and in a single step. This demonstration adds a novel easy-to-implement native three-qubit logic gate for universal quantum computing.

Emulating impossible 'unipolar' laser pulses paves the way for processing quantum information

Tue, 24 May 2022 16:48:42 +0000

A laser pulse that sidesteps the inherent symmetry of light waves could manipulate quantum information, potentially bringing us closer to room temperature quantum computing.

Scientists use quantum computers to simulate quantum materials

Tue, 24 May 2022 16:46:33 +0000

Researchers have used quantum computers to simulate spin defects, an important material property for the next generation of quantum computers.

Christine Muschik, Crystal Senko awarded Early Researcher Awards Funding

25267 — Tue, 24 May 2022 00:00:00 +0000

Tuesday, May 24, 2022

Developing quantum simulations of particle interactions and trapped ions are two Institute for Quantum Computing research projects broadening disciplinary horizons and delivering real-world impact. Waterloo scientists Christine Muschik and Crystal Senko each received funding through the Government of Ontario's 2022 Early Researcher Awards program.

Interplay between charge order and superconductivity at nanoscale

Fri, 20 May 2022 11:31:38 +0000

Scientists have been relentlessly working on understanding the fundamental mechanisms at the base of high-temperature superconductivity with the ultimate goal to design and engineer new quantum materials superconducting close to room temperature.