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Recently there is news concerning some computational breakthroughs by using so-called "entropy quantum computing":

https://thequantuminsider.com/2022/07/20/qci-solves-3854-variable-problem-in-six-minutes-in-bmw-group-aws-quantum-computing-challenge/

But I cannot find any discussion or description about entropy quantum computing in the Internet, except in that company's (QCI) website (but which is very brief and superficial). Could anybody point me to any reference, technical or not, concerning entropy quantum computing?

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    $\begingroup$ I wouldn't call a mere press release of a publicly traded company a breakthrough. $\endgroup$
    – MonteNero
    Commented Jul 29, 2022 at 19:33
  • $\begingroup$ I think if you cool down ordinary Intel processor in liquid nitrogen it computes times faster than at room temperature, and its productivity is comparable to those quantum devices. $\endgroup$
    – kludg
    Commented Jul 30, 2022 at 7:18
  • $\begingroup$ Quantum Computing Report also reported this case recently, and seems also to have no idea what EQC is, and hopes "that more information about this Entropy Quantum Computing concept will be disclosed by QCI in the future." $\endgroup$
    – QGK
    Commented Jul 31, 2022 at 15:37
  • $\begingroup$ QCI just uploaded a new YouTube video introducing their "Dirac 1 Entropy Quantum Computer", in which a technical paper is referred. This paper is the only technical reference that OCI has revealed up to this moment about their entropy quantum computing. $\endgroup$
    – QGK
    Commented Sep 20, 2022 at 22:59

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I've never heard about "entropy quantum computing" before. Both the links you provided only give extremely generic information, mostly devoid of details, together with rather bold claims. A few comments on what I've managed to gather reading the website and quickly going through the presentation linked there:

  1. They claim to have "solved an optimization problem with over 3800 variables in six minutes, delivering a superior and feasible solution. The Company achieved this landmark by applying a new quantum hardware technology called Entropy Quantum Computing (EQC)". They then write that "The EQC ran over 70x faster than QCI’s 2021 hybrid DWave implementation." So it seems like they are solving optimisation problems with their device. They don't seem to be saying it explicitly, but it seems likely that they're therefore using some special-purpose hardware more or less tailored to the kind of optimisation task they're dealing with. Given the comparison with DWave, probably a hybrid classical-quantum solution (but again, this is just what I'd guess, I don't see them saying this explicitly).

  2. Regarding how their system operates, they write:

    EQC operates on the most fundamental principles of quantum physics, especially its measurement postulate, wherein the wavefunction of a quantum system will collapse to a certain eigenstate due to its interaction with a measurement apparatus or, broadly speaking, the surrounding environment.

    Well... I'm confident that this sentence is not false. Then again, I'm not sure how else a quantum device would be supposed to operate. By which I mean, they're really not giving very useful info here. They then continue writing:

    However, while existing quantum computing architectures must operate on closed quantum systems under extreme requirements to calm the effects of the environment, EQC operates on open quantum systems, carefully coupling a quantum system to an engineered environment, so that its quantum state is collapsed to represent a problem’s desirable solution.

    This does actually some clues. Apparently they're claiming to use open system dynamics, in a way that is quite noise-resilient. There are schemes to engineer environments in order to drive a system towards a certain state. A keyword here is quantum reservoir engineering. Although they're writing that the quantum state is "collapsed to represent a problems' desirable solution" confuses me. If they're doing some sort of reservoir engineering, which means, roughly speaking, to create a coupling with an environment such that a certain target state is a fixed point of the evolution, talking about "collapse" seems a bit weird to me. This aside, in this sort of scheme I'd guess that the problem parameters are specified through interactions between reservoir and system, which therefore does not really have an "input" like gate-based models do.

    For a scheme like this, a compelling question would be how easy it is to find the parameters to use to tune the dynamics in order to obtain the wanted result. And how to figure out what sorts of dynamical processes both have a good resilience to noise, and allow you to manageably figure out interaction parameters resulting in the solution of a given optimization problem, using only the details defining the optimization problem itself (and hopefully, without having to solve the optimization problem itself in order to find the parameters allowing the device to solve the optimization problem).

  3. In the presentation they mention that they used "photonic systems" provided by the company QPhoton. Can't find much information about this company.

  4. Around 4:05 they say that "entropy quantum computing is deeply rooted in quantum physics and aligns very tightly with the measurement postulate". Not sure what to make of this. The notion of entropy is obviously "common" in quantum (or classical for that matter) physics, but I'm pretty sure they invented the term "entropy quantum computing" themselves.

  5. The most detailed description of the method is around 5:03 in the presentation, where they say that (emphasis mine):

    we use [our entropy quantum computer] as a free source of energy and we engineer a tightly controlled interaction with the environment. When you do that you get a system that allows you to unlock a lot of very powerful quantum operational features. [...] The real secret to how the technology work is to very carefully couple the system, it's a photonic system, to the engineered environment which we'll call the entropy. And that quantum state is allowed to relax or collapse to a desired solution across the many quantum modes in the optical system. We achieve full operation at room temperature and because there are so many photonic modes available to us we can solve very large [variable states?] problems.

    They then say

    [...] we consume the problem by directly feeding the Hamiltonian into the EQC, no preprocessing or postprocessing software involved, and this controlled feedback, or backaction, from the environment to the system, allows us to produce ground state information for the objective function and capture all of the constraints in the solution which we subsequently analysed. The total runtime was just over 6 minutes.

    This is the most interesting part in my opinion. A few comments:

    1. They say they have a photonic system with many photonic modes. This points towards a continuous variable kind of scheme, maybe akin to what other companies such as Xanadu are doing. Xanadu also claims their scheme to allow for fault-tolerant room-temperature quantum computation, as per https://arxiv.org/abs/2010.02905.
    2. I'm puzzled by their saying that there is no pre- or post-processing involved, but also that they use a feedback system. I suppose what constitutes "processing" is subjective? Still, the use of a "controlled feedback from the environment" and "produce ground state information" points towards a hybrid optimisation approach: evolve the system a bit, measure to see how it's currently doing, tune the parameters of the interaction accordingly, and iterate.
    3. Referring to the "engineered environment" as "entropy" is... puzzling. I suppose they couldn't just talk of "reservoir computing" because that name is taken to mean something else?
  6. The claims are from a public company (QUBT) traded on the NASDAQ. Make of that what you will.

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  • $\begingroup$ Thanks indeed for your detailed responses. My replies: 1. The situation is somewhat puzzling to me since on the one hand, the so-called EQC seems to be not mainstream methods in QC, yet claimed to have superior performance, but on the other hand, one has few clues what exactly EQC is. A natural response: is it real? But at the same time, making unfounded bold public claims like that would be outrageous. 2. Would their stress on measurement be related to measurement based QC? To be continued below... $\endgroup$
    – QGK
    Commented Jul 30, 2022 at 12:01
  • $\begingroup$ 3. Your guess of their using hybrid devices tailored made for optimisation problems is, I think, justified, as revealed by other info in their website. 4. As you pointed out, open system dynamics (or maybe the reservoir computing approach you mentioned) seems to be the key to figure out what EQC is. But it has not solved my puzzle since I don't know if this kind of approach could achieve what EQC is claimed to be capable of achieving. Also, I cannot relate (at least primarily) the publications of the key person, Prof Yuping Huang, of QPhoton to this kind of approach. To be continued below... $\endgroup$
    – QGK
    Commented Jul 30, 2022 at 12:24
  • $\begingroup$ 5. Your mentioning of Xanadu may be insightful because I have another source, who might have some internal info, also comparing Xanadu with that company, QCI. But then: QCI can achieve some technological level that Xanadu cannot? If so, is it reasonable to believe so? $\endgroup$
    – QGK
    Commented Jul 30, 2022 at 12:34
  • $\begingroup$ as far as I'm concerned, as of yet, there's no reason to believe there's anything worth noting here. And no, I also wouldn't say the situation is particularly "puzzling". Similar bold claims from companies that provide no details are certainly not rare. Honestly, I think me trying to understand more about it in the website and presentation was mostly driven by the fact that I found the name "entropy quantum computing" kind of funny. $\endgroup$
    – glS
    Commented Jul 30, 2022 at 20:00
  • $\begingroup$ @glS, indeed funny name. $\endgroup$
    – MonteNero
    Commented Aug 1, 2022 at 4:43

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