Recent comments from SciRate

Exciting work! May I kindly ask if you thought about making your optimization code publicly available? I believe that it would make using your methods much easier for a lot of people!

This is a clever and interesting paper. I am not a proponent of the "consciousness causes collapse hypothesis" (CCCH), but I nevertheless want to remark that somebody who is might attempt a defense of the CCCH along lines of the following:

Take the CCCH to say that wave-function collapse occurs w

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Cedric,

1) In Theorem 27, we show that if a promise problem is recognizable in O(log n) space and poly(n) time by a family of *general* quantum circuits, then it is also recognizable in O(log n) space and poly(n) time by a family of *unitary* quantum circuits. A bound on the hidden constants in t

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Great work! I haven't had time to gone through the paper in great detail, but I have two immediate questions:

1) You mention in the abstract that the procedure is simultaneously space-efficient and time-efficient, but I couldn't find a statement of this in the main body. Could you be more precise

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https://qbnets.wordpress.com/2020/06/07/anomaly-detection-with-quantum-computers-better-than-with-classical-computers/

A short talk based on this work is scheduled today at 16:30 (UTC+2): https://www.youtube.com/watch?v=h4uFaV6rFSc

More details (including a 3-page abstract) here: https://www.monoidal.net/paris2020/talk/qs12t1.html

Here's a short talk based on this, given today at QPL 2020: https://www.youtube.com/watch?v=uO08ci5dK6Q

From my perspective, everything until Section 5 follows directly from the fact that the group $\mathrm{DS}(2^w)$ (aka *real Pauli group*) as well as the projective and normal Pauli group form unitary 1-designs and thus a tight frame for the space of complex matrices which gives you the desired Parse

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It would be very interesting if one can show and realize the maximally localized Wannier functions in a digital quantum simulations.

Great work, please do keep it up! Minor gripe about abstract statement - the sentence about experimental systems is misleading, as far as I understand due primarily to the difficulty of reliable single-photon sources in the lab. I realize you say "in principle", but to the average CS person like mys

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The orthonormal operator bases defined in Eq. (9) were previously studied by Zhu, who proved that they saturate bounds defined using the negativity of quasi-probabilities [PRL **117** (2016), 120404, [arXiv:1604.06974\]][1].

[1]: https://scirate.com/arxiv/1604.06974

Simons Apers's talk at the Simons Institute: [https://simons.berkeley.edu/events/quantum-speedup-graph-sparsification-cut-approximation-and-laplacian-solving][1]

[1]: https://simons.berkeley.edu/events/quantum-speedup-graph-sparsification-cut-approximation-and-laplacian-solving

https://arxiv.org/abs/quant-ph/9805016
How to Compile a Quantum Bayesian Net

I am unsure about the reasoning presented in this paper. It seems to me that there is an issue with the reasoning used to upper-bound the query complexity of the multi-layer quantum search method.

On page 4, you begin by lower bounding the number of queries Q of the algorithm by a value Qmin (eq

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Nice work. By the way, your paper's arxiv metadata abstract field has a redundant copy of part of the abstract.

Proposition 7 for the SLD information is a known property called extended convexity; see S. Alipour and A. T. Rezakhani, PRA 91, 042104 (2015), http://dx.doi.org/10.1103/PhysRevA.91.042104. We proved it for multiple parameters by relating it to the strong concavity of the fidelity; see Ng et al., PR

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Thanks very much for your comment Jerry. As with other table-top tests of quantum gravity, we are proposing an experiment in which all forces other than gravity, e.g. the electromagnetic force, can be neglected. Then, due to the universal coupling of gravity, gravity couples to the kinetic and mass

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much thanks to Craig Gidney, for pointing out that the only linear-depth Clifford component can equivalently be implemented in logarithmic depth

Indeed, just focusing on T complexity is a bit too simplistic. I only quoted that number because your abstract focused on T complexity. The tradeoff between gate complexity and space complexity is why the abstract of [arXiv:1902.02134][1] emphasizes the reduction in surface code spacetime volume (a

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Hi Ryan,

I agree with your argument that the $10^{15}$ value is outdated at this point. I'll post an updated version that better reflects this. However, the $10^{11}$ figure is a swing too far in the opposite direction, I think. Even though the algorithm in [arXiv:1902.02134][1] is the most minimal

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The $10^{15}$ figure in the abstract refers to an older algorithm. More recent work has brought estimates for FeMoco to around $10^{11}$ (see [arXiv:1902.02134][1]), albeit with some ancilla (but even a version with very few ancilla improves from $10^{15}$ gates). That new work is based on qubitizat

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This is certainly an important result. However, it does seem to require the assumption that "the classical interaction would not induce quantum self-interaction". So I wonder how certain we can be that, for example, classical gravity might not induce a very small term (such as suggested in footno

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The code implementing the angle finding algorithm, together with an example of Hamiltonian simulation, is available at [GitHub][1].

[1]: https://github.com/alibaba-edu/angle-sequence

I was sure that somebody had won an Ig Nobel for doing this, but I think my memory mixed up the levitating-frog experiment with a stunt that they did back in the day at the MIT Magnet Lab, lowering the temperature of a whole workshop with LN2 until they could Meissner a magnet off the floor.

After reading footnote [32], I would like to enthusiastically urge everyone to cite this paper heavily via every medium. My homestead on the Tozitna River in interior Alaska is, in fact, engaged in a long-running and ongoing war with beavers. The dam critters keep building dam after dam across the

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A breakthrough!

now this is one paper on QKD that I think deserves many scites! :)

Tensorflow Quantum combines Tensorflow with [Cirq](https://github.com/quantumlib/Cirq). Are you asking for Cirq to be combined with pyTorch as well?

Cool, thanks. Can now somebody please port it to pyTorch?

https://qbnets.wordpress.com/2020/03/09/google-releases-tensorflow-quantum/

A quick question. Volume is complexity. Then the time that Alice needs to measure the volume is the complexity of complexity, just like

>Complexity of complexity is the number of simple logical steps that it would take to confirm
$\mathcal{C} \le \mathcal{C}_0$

Then why can we assume

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Thanks, Edgar, Vishal, for your feedback!

The title is an attempt at humor - no sensationalism intended. I don't think that a technical paper on gate decompositions will be understood as taking a stance on the validity of mystic medicine. (In the same way that the title of Scott Aaronson's book i

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I agree that scientists should suppress their tendency to *excessive* sensationalism, but I don't know that this title crosses the line. In fact, in this case, it seems to be a relatively apt description of the title and amusing to boot. It certainly caught my attention.

Perhaps a note somewhere

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I agree.

Perhaps it would be good to reconsider the title. As scientists, we should not succumb to the temptation for sensationalism - and we should try to mark a clearer distinction between real science and pseudoscience.
I think the work you did here is really impressive, and the title removes some of thi

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Takes a task that I thought required complicated self-testing techniques and achieves it with a beautifully simple protocol that requires almost no technology for the analysis. This should be taught in classes.

Regarding the name, I actually prefer "shadow estimation" for this task. (I suggested this as well to Scott, but he went with "shadow tomography" instead.) This is because tomography is the inverse problem of taking a shadow and reconstructing the object that produced the shadow. The term "shadow" d

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Good observation! The main theoretical result we proved for predicting quadratic functions with random Pauli measurements is given in Example 1, Appendix (A3). The number of measurements needed to predict $M$ quadratic functions to $\epsilon$ additive error is $\mathcal{O}(\log(M) 4^k / \epsilon^2)$

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Congratulations on this very exciting work! I have a question regarding the second Rényi entropy measurements in Figure 4b. The error in Brydges et al. scales like $\frac{1}{\sqrt{\text{num. of exper.}}}$ as expected. However, the figure suggests that the shadow protocol's error scales as $\frac{1}{

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I completely agree that the quantum problem has interesting new nuances and I look forward to reading your work as well as related papers; I'm just not sure about the necessity of the name "shadow tomography."

Regarding the issue of non-commuting observables, it is interesting to note that the Ho

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From my understanding, semiparametric and nonparametric estimation focuses on the best approach to estimate a single (complicated) function of the underlying statistical object (probability distribution or quantum states). When we want to estimate a single linear function in the quantum state, Tr(O

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Correct me if I'm wrong, and I'm not questioning the novelty of recent works of this nature, but I think there's no need to create a new name such as "shadow tomography" for this type of problems. Classical statisticians have been studying the estimation of probability density functionals for many d

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The abstract differs slightly from the abstract in the article. Please refer to the abstract in the article for the most recent version:

We report, in a sequence of notes, our work on the Alibaba Cloud Quantum Development Platform (AC-QDP). AC-QDP provides a set of tools for aiding the developmen

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Here is the submitted TQC extended abstract, if you want to read a 3 page summary
https://earltcampbell.files.wordpress.com/2020/02/tqc_abstract.pdf

Nicolas, Christopher,

Thanks for your responses. The previous papers you cited assume no syndrome measurement error, which is an idealization. It's not obvious to me what the results in those papers would have been if syndrome measurement error were included.

Conversely, this new paper does co

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Sorry for the confusion, but as I mentioned in our earlier discussions, our paper [WMHY19](https://scirate.com/arxiv/1902.00869) is based on a different framework, and I'm not sure if they can be compared directly. As a simple illustration, the wiki page of ['AdaBoost'](https://en.wikipedia.org/wiki

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In fact, twirling when one has coherent noise can make the performance worse (see https://arxiv.org/abs/1612.02830).

That's correct. We focus on bit flips or Pauli noise. This seems reasonable since coherent errors generally do not degrade too much the performance of error correction as one can see with your repetition code paper [https://arxiv.org/abs/1612.03908] or with the surface code study of [https://arxiv.o

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This paper appears to consider only stochastic errors, i.e. bit-flips with probability p. Have you thought about how the results would change for coherent errors?

Shameless self-promotion: I studied the same problem (quantum estimation of one parameter among many unknown parameters) and also used geometric concepts extensively in this work:

https://scirate.com/arxiv/1906.09871 (first version on 24 Jun 2019, last update on 6 Feb 2020)

Now that Carl is wo

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