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1

Both IBM and Google unveiled 53-qubit processors. At this time, only Google published performance metrics such as 1- and 2-qubit gate errors. Until IBM publishes similar metrics we simply cannot even tell whether Google's processor outperforms IBM's. What we can tell is that the connectivity of the two processors is different - Google's Sycamore processor ...

-2

If I understood it correctly, Google designed its quantum processor for particular task while IBM's Q Experience is an universal quantum computer. There are more single purpose quantum computers, e.g. D-Wave quantum annealer. However, this computer is designed only for optimization tasks. Of course, in comparison with IBM Q, this is more powerful device but ...

2

My guess is that this is an example of co-opetition, i.e. collaborative competition. Number of qubits is just a single characteristic of a quantum processor, but there are a lot more, like tolerance, topology, etc. Also this characteristic is the only one that most people understand. Thus it's not reasonable to put all the resources on the increasing just ...

10

It's just a coincidence. I can speak from personal recollection on the Google side. Google originally intended to use a 72 qubit chip (Bristlecone) where qubits were essentially directly connected to each other. They then switched to an architecture where qubits were connected indirectly via a coupler. The coupler requires a control line, so this increased ...

1

I'm sure that this has something to do with quantum decoherence or "noise" which is caused when more qubits are added. It's likely that they are both at the frontlines of research so 53 qubits are the best that they can do given the hardware that they have access to. As they add more qubits it gets tougher to compute and prompts them to find some suitable ...

2

In fact, you would need an astronomical circuit depth in order to get close to a uniformly random state, or even close to a randomly chosen probability distribution on the $2^{53}$ outputs. As a first estimate, consider how many different distributions you need in order to be within 1/8 of the total variation distance of any distribution on $N$ outputs. ...

3

In the framing of the question (which I believe to be asked in good faith), there seems to be at least two objections. Sampling from a set of strings is not clearly a function, and Sampling is a physical process, outside of computation. Initially, with regard to the first objection, I assert that sampling is a function, as a search problem. For example, ...

3

The Church-Turing thesis is not in and of itself a rigorous concept, but rather a judgment on rigorous concepts of computability. As such, it's negotiable. The language in Rosser's 1939 expository paper about provability and computability is biased towards deterministic algorithms. There is an important simplifying theorem here: If you only care about ...

4

A computational task doesn't have to have or be an application in order to be part of a valid model. If you claim that you can run a mile faster than I can, your four-minute mile doesn't have to be profitable employment in order to count. On the other hand, the random sampling demonstration with Sycamore certainly is an action of some kind performed by a ...

6

They say in Section X.H of the supplement that the Summit supercomputer has a power capacity of 14 megawatts. They compare that to their own setup. Their power consumption is mainly their dilution fridge, which they say is about 10 kilowatts plus about another 10 for chilled water for its supporting equipment. Their own supporting PCs and other ...

2

After some further consideration I think it's quite clear that the only probability mass function evaluated in the computation of $\mathcal{F}_{\text{XEB}}$ is that of the classically computed ideal distribution, denoted $P(x_i)$ in the main paper. This leads me to the conclusion that the phrasing of the following excerpt from section IV.C of the ...

3

Paraphrasing some tweets on the matter earlier, the result is rather underwhelming because it plays on a discrepancy between what they mean by quantum supremacy (QS) and what people tend to think QS means. What I find most people think QS is supposed to mean, and what I assumed it meant until a month or so ago, was that there exists a computable problem (in ...

15

Google's paper/results are kind of sideways to questions in computational complexity about the relation between $\mathrm{BPP}$ and $\mathrm{BQP}$ (and even further from questions about whether $\mathrm{P}\ne\mathrm{NP}$). It's more as if Google relies on the hypothesis that $\mathrm{BPP}\ne\mathrm{BQP}$ as evidence that their quantum computer performs a ...

4

That seems to restrict the output probability distributions of all quantum circuits to rather high entropy distributions. The output of a typical randomly chosen quantum circuit is rather high entropy. That doesn't mean you can't construct circuits that have low entropy outputs (you can), it just means that picking random gates is a bad strategy for ...

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