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?
There's a commercial product for classical simulation of small QPUs [0]. With dedicated hardware handles up to 41 qubits. I'm not affiliated with the company or endorsing their products, just pointing out that they exist.
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.
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).
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.
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.)
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..
I spent a few years at Caltech in an academic role (not as a student). I never understood the rationale behind the unsupervised take-home exams. Could you elaborate what is it that makes it a 'success', in your opinion?
The shortcomings that seem obvious to me are:
- Penalizes honesty
- Implied notion that Caltech students are "honourable" and honest. How is this achieved in practice?
I think your question is excellently phrased. The answer for anything data science-y is "no." The bottleneck will be transferring the input data onto the quantum CPU.
For algorithms like HHL that have superclassical performance, a complex superposition encoding the data needs to be created first. This state is subsequently "consumed" by the algorithm. The no-cloning theorem forbids creating copies of the encoded state, and hence the encoding step needs to be repeated every time the algorithm is run.
For another example, consider Grover's search that is sub-linear in calls to an oracle function. If the oracle references a linear array of data, for example, it needs to work on superpositions of array indices. In other words, the entire dataset needs to fit in "quantum" memory.
Using a quantum cpu can only be sensible for computationally difficult problems where the hard problem instances can be specified by a relatively small number of bits.
What seems to be missing from the replies posted so far is the notion of coherence, and the choice of measurement basis.
What separates a coherent "quantum" superposition, say, |0> + |1>, from a probabilistic "non-quantum" 50:50 mixture is that I can choose a measurement basis in which the coherent state always yields a definite result, say "1", whereas measuring the mixed state always yields a 50:50 mixture of "0"s and "1"s.
A continuous sweep of the angle of the measurement basis generally results in an interference pattern, the amplitude of which can be used to assess the fidelity of the quantum state.
(I get paid to work on quantum communication and related experiments.)
I used to be puzzled by how anyone could ever drive off with the nozzle still in the car. I had only seen the non-latching dispenser, and service stations don't exist in my country. I was probably (shamefully) thinking something along the lines of "dumb Americans."
Once in the US, I realized it's a mixture of full service and self-service stations with latching or non-latching dispensers, depending on the region. And I do need to make an extra glance at the gas tank door before driving off.
Agree with sentiments of the parent comment. I believe the prestige and social standing of physics (and, in part, the academia as a whole) is founded in the truly transformational technological advances -- say, the transistor -- made in the past decades.
Problematically, to secure funding today, one is essentially expected to frame every condensed-matter experiment as the next transistor. Not only in grant applications, but increasingly also in the abstract and opening paragraphs of research articles. There's a marked contrast with older research articles in physics, which usually go straight to disseminating the results. (Needless to say that I prefer the old style.)
As a result, a great deal of funding and attention is allocated towards projects that simultaneously 1) Will not improve the quality of life of anyone, even in the long term. 2) Are "de-risked" to such extent that no new scientific insights can come out of them.