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Reading the competition rules, the prize will be awarded for algorithms that do the most societal good with the smallest estimated size of quantum computer - There is no requirement to actually execute the proposed computation. Also they give no time horizon by when the hardware should exist. So, it's very possible that no "actual uses" for quantum computers are known even after the prize has been handed out.

I agree with parent (despite the downvotes), there's an ontological mismatch in the title. Mathematical constructs by themselves cannot reveal truths about the physical world. Instead: Mathematical model confirmed by experiment. Or: Old observations elucidated by mathematical model.

No quantum computing platform today has meaningful error correction. What you can get, in some cases, is limited error detection based on the outcome of the question computation, if it's built into your specific algorithm.

What would be the example of an outstanding, reasonably-sized (even quantum computers of the future have finite resources) quantum simulation problem that is intractable today but would unlock some economic potential, if solved?

You can create a free account at IBM Quantum and peek at the latest calibration data there.

Edit: Only a fraction of the qubits of the 127 qubit system were calibrated when I looked.

For anyone struggling to make sense of this: I have a physics PhD, I have spent most of my career in fundamental research, I have read the Wikipedia article and other references multiple times, and attended several talks, and I still don't understand what qualifies as a time crystal. All I know that it seems to have something to do with subharmonic response.

This is quite a convoluted way of making an electrical switch. In brief, the carriers of supercurrent (Cooper pairs) in the channel are "depleted" (actually: broken up) by a local heater that raises the channel temperature above the superconductor's critical temperature.

The circuit topology, a heater coupled to a superconducting wire to sense the local tempreature, is the same as in a transition-edge sensor (TES), and functionally identical to a supercondicting nanowire single-photon detector (SNSPD). [SNSPD does not have a separate heater.] These cryogenic detectors are typically used when a good electrical amplifier does not exist for the input radiation. A textbook example would be optical or x-ray photons.

I believe the device will be terribly inefficient as a transistor. The root cause is that the electrical signal gets converted into heat and back: Heater current -> Electron heating -> Phonon (lattice) heating -> Breaking of Cooper pairs in the channel -> Suppression of (super)current. Once the heat is in the phonons (lattice vibrations), it can propagate anywhere in the chip substrate.

Also, the active area needs to be continuously heated to maintain the resistive ("off") state.

Since HN is mostly a computing-oriented forum: This transistor will not be used for general-purpose logic cricuits.

I appreciate your views otherwise, but I did not use or imply "fraud" in my comment(s).

Not the person you're replying to, but I admit to characterizing your paper as "garbage" in another comment thread. Since you're inviting discourse, which I greatly appreciate, I'm compelled to reply.

1) To anyone who's studied algebra, it is clear that identities of the form LHS = RHS can be obtained by a nested application of transformations and substitutions in a consistent manner.

2) Of course, arriving at a new, insightful result often involves taking mundane steps. However, in this case, the new mathematical discoveries based on the output tableaus of your algorithm are hypothetical. Whereas the manuscript (and the authors) have already pocketed one of the premium accolades in sciences in the form of a Nature publication.

3) To drive the point above home, do you think the resulting mathematical insights themselves, without riding on the "AI" novelty aspect, would clear the bar for a Nature (or similar high-impact) publication? To be clear, I'm not a mathematican, but I believe the answer would be no. Contrast this with another AI/ML advance published in Nature quite recently: AlphaGo. Note how the gist of their paper, superhuman performance in Go, is a self-standing achievement that merely makes use of machine learning techniques.

Just posting to lament the fact that there are two blatantly garbage-tier research articles on the front page at the moment (this and the Ramanujan machine).

> How is this achieved in practice? Wish I knew.

I think my take-home from this discussion is that the honor code can be made to work in the right circumstances that exist, at least, at Caltech, Rice, etc.

At the same time, I believe it is impossible to induce the requisite "cohesion" in other contexts such as 100% remote learning or high-stakes mass testing (entrance exams etc.), even if the student body stayed the same.

I was aware of the implications, hence the disclaimer "(Bad if ...)"

However, I'd assume that doing either during a test would be against the honor code.

My counterarguments, in good faith:

Clearly, any society collectively benefits from honesty, whereas an honest act is, at least in a strict game-theoretic view, a loss to the individual in the short term.

The question "Are you honest?" does not necessarily filter out dishonest people. (Incidentally, I always felt like I would not have been admitted to Caltech as a student.)

Interesting, thanks for the replies.

I'd still point out that the system is not robust against: - Working in groups. (Bad if tests are supposed to assess individual performance.) - Asking outsiders for help.

Also, your downplaying of "rote" learning feels misguided, no matter how advanced/abstract/high-level the domain in question is. Cue the Bruce Lee quote about 10,000 kicks..