Recent comments from SciRate

Dear Tom,

Thanks a lot for your prompt response and also for engaging in this experiment to asses whether SciRate can be a feasible platform for reviewing papers publicly while engaging with the authors during the process.

I agree that adjacency matrix and Laplacian are two natural choices of

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Dear Referee,

Thank you for taking the time to carefully referee the manuscript. I appreciate your organized response and the opportunity to clarify the motivations and results. First, let me respond to your two main points:

1. I like your suggestion of considering more elaborate search algori

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**Summary**

This paper investigates continuous-time quantum search on a graph with several marked vertices. Using a specific family of graphs, it is shown that different marked vertex configurations can lead to different optimal choices of parameters in the Hamiltonian governing the walk, and thu

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There's some code for this here: https://github.com/ryankiros/skip-thoughts

This paper "**Tree-based convolution for sentence modeling**" is a deliberate plagiarism. The texts, models and ideas overlap significantly with previous work on arXiv.

- TBCNN: A **Tree-based Convolutional** Neural Network for Programming
Language Processing (arXiv:1409.5718)
- **Tree-based

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There's also a [docker image](http://ryankennedy.io/running-the-deep-dream/) if you want to play with it, though if you're on Linux or OS X you might want to install everything natively in order to get GPU acceleration (the gradient ascent can be quite slow on higher layers in the network)

The image recognition model described here is the one responsible for [deepdream](http://github.com/google/deepdream).

![deepdream nebula][1]

[1]: https://pbs.twimg.com/media/CI_EASXWcAAGXnK.jpg

Wow, impressive. Very impressive.

I like the word FORANDLATION: it's a corruption of a corruption of the word "correlation".

Interesting comment at the bottom of page 9... :)

This article contains new results concerning the stroboscopic tomography, the aim of which is to reconstruct the initial density matrix on the basis of the least possible amount of data. The author proposes one complete quantum tomography model, which shows how step by step you can obtain the formul

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Thanks for the comment! This is a good idea, I will do that in the next arXiv version.

Wonderful! I've been waiting for a book like this for a while now! Thanks, Marco.

I do have one trivial comment from a 30 second preliminary scan, though: please consider typesetting the proofs with a font size matching the main text. If us readers are already squinting hard trying to understand

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The authors will want to look at work that Simone Teufel has done, in particular her Argumentative Zoning, which discusses the stance that the paper author takes with respect to the citations in that paper.

This article has generated a fair bit of discussion. But I found a few of the statements puzzling (Edgar Lozano also). Take for example, Theorem 1 (ii) (reversibility) which appears to contradict a number of previous results. Should we understand your work function as "work in the paradigm where we

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Thanks for the further comments and spotting the new typos. To reply straight away to the other points:

First, the resulting states might as well stay in the same bin (even though, as you rightly note, the bins no longer correspond to the same bit-strings as before). All that matters is that the

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Thanks for updating the paper so promptly. The updated version addresses all my concerns so far. However I noticed a few extra (minor) things while reading through it.

On page 15, last step of 2(b): if $|\psi_r\rangle$ and $|\psi_t\rangle$ were in the same bin but the combination operation failed

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Thank you for these very detailed and helpful comments. I have uploaded a new version of the paper to the arXiv to address them, which should appear tomorrow. I will reply to the comments in more detail (and justify the cases where I didn't modify the paper as suggested) when I receive them through

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From the abstract: "Our result suggests that the coherent-state scheme known to achieve the ultimate information-theoretic capacity is not a practically optimal scheme for the case of using a finite number of channels."

I find this language highly misleading and would have appreciated an arXiv po

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Finally a good use for the Torsion tensor

**Summary and recommendation**

This paper considers a $d$-dimensional version of the problem of finding a given pattern within a text, for random patterns and text. The text is assumed to be picked uniformly at random and has size $n^d$ while the pattern has size $m^d$ and is either uniformly ran

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Thanks for the clarification. In fact it seems that I do have this option switched on, with the correct author identifier, so I'm not sure why I didn't get an email about these comments.

First of all let me say that I find the paper very interesting (which is why I read it in the first place), but at this stage it's not obvious to me that it will work; extraordinary claims require extraordinary evidence!

Your measurement (Eqs. (12) and (13)) has a classical Fisher information that

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Mankei, thanks for your comment.

Indeed, it is common knowledge that the optimal POVM given by Braunstein and Caves in general depends on the unknown parameter. An adaptive method can always be used to achieve the equality $F = F_q$. This issue has been addressed many times in both theoretical an

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I've seen this mistake so many times that I have to say it in public: The quantum Fisher information in terms of the symmetric logarithmic derivative ($F_q$) is NOT equal to the classical Fisher information optimized over all POVMs ($F$), contrary to the claim by Braunstein and Caves PRL 72, 3439 (1

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Hi Ashley,

Thanks for your reply, it was very helpful! I thought about e-mailing you but I wanted to preserve my confidentiality as a reviewer. Also, I wanted to see if it is feasible to use SciRate as a platform for interacting with authors during the review process.

I encourage you (and **ot

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Hi,

Thank you for your very detailed comments / questions about the technical points in this paper. I did happen to check Scirate today but in general (as I suspect with many other people) I don't check it regularly, so for reliable replies it's better just to email me. To reply to your questions i

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Hi Ashley,

I hope you are actually checking SciRate from time to time because I have some more questions about your paper.

1. In the proof of Theorem 2 (on page 7) you say that *it is immediate* that RoughCheck uses a certain number of queries. Shouldn't the pre-factor be $\nu^d$ rather than $\nu

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Hi Felix,

Thanks a lot for your comments. Yes, $X$ can be any state and we've made this clearer now in the paper - we don't need to assume that it is diagonal.

We've also noted more prominently that achievability of our result in the full thermodynamics case is only when the target state is b

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Hi Ashley,

I'm reviewing your paper and I have trouble understanding some very basic definitions on page 3 (paragraphs 2 and 3).

1. First, I can't parse $k \in [n-s]$ where $n$ is an integer and $s$ is a string. I assume instead of $s \in [n]^d$, $k \in [n-s]$ you mean $k = 1, \dotsc, n$ and

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I put this on my reading list after the recent update, having a casual interest in foundations. While I don't have quite enough physics background to see if anything is being swept under the rug, I found it an interesting point of view, written clearly and without mysticism. In particular, I now un

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A question to Theorem 1:

In the process description in eq. (2), you mention that $X$ is any arbitrary state. However, in the proof of Theorem 1, in the converse part you assume that $[X,\sigma]=0$, or equivalently that $X$ is diagonal in the eigenbasis of $\sigma$. Similarly, in the achievability

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This is a preliminary version and I am happy to incorporate feedback I receive in the coming month. Any comments are welcome.

This paper apparently solves a problem we posed in http://arxiv.org/abs/quant-ph/0008070. That's great, though it's kind of confusing that they decided to use exactly the same title.

The strange equation is supposed to look like this:
$$f(\sqrt{a} X + \sqrt{1-a} Y) \geq a f(X) + (1-a) f(Y) \quad \forall a \in [0,1]$$

Neither, Frédéric! Replacing fidelity by superfidelity still requires optimizing over all density matrices. However, the Birkhoff-von Neumann Theorem (see Lemma 1) allows for further restricting this optimization to n scalar variables w.l.o.g.---Theorem 2. Arguably, this greatly simplifies the geome

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Daniel Nagaj presented it at Qcrypt 2014 : his slides are here http://2014.qcrypt.net/wp-content/uploads/Nagaj.pdf , with the corresponding video here https://www.youtube.com/watch?v=PE0vco9_BBM

I fell for that clickbait title and read the paper. I still don’t get why von Neumann didn't want us to know about this weird trick? And which weird trick? The use of superfidelity or the use of non-physical density matrices like $\sigma^\sharp$?

I don't think the paper does what it claims doing, especially after reading:
>As known, there exists an entire class of these problems which is termed NP-complete (non-deterministic polynomial time complete) because the computational effort used to find an exact solution increases exponentially as

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I took the liberty of uploading the IPython notebook as a github [gist](https://gist.github.com), so it's viewable [here](http://nbviewer.ipython.org/urls/gist.githubusercontent.com/silky/b14fa42c6d5475a3a724/raw/887c19fb04581f1a33f9d03370e4b7b3a33c2ea8/ferrie_kueng_bayes_est_fid.ipynb).

Well, this MUST be scited :-) I am surprised it was not already!

frod prefect, a perfect fraud.

I'm pretty sure this first author was just entirely made up ...; but is the error in the name intentional? Weird!

A very similar problem was considered in detail in http://arxiv.org/abs/quant-ph/0111153

This is also called the Hilbert-Schmidt norm (and metric), and its properties are quite well established already...

The manuscript has been widely revised to focus the reader's attention on the proposed method and its application in presence of local disorder.

Best,

Salvatore, Gian Giacomo and Alán

The basic idea of this paper is to test whether the decimal digits of three special constants $(\pi,e,\sqrt2)$ act as though chosen from a uniform distribution, based on their first ten million digits. In particular the author studies the sum of the digits compared to the expected behavior by the la

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@rrtucci
The algorithm in your reference paper requires knowing in advance the exact number of marked items, represented by the quantity \gamma (to use your notation). The rotation angles (or phases) of the reflections depend on this quantity via equations (82) and (89) and is also explicitly state

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Note: The arXiv reference in the abstract should be arXiv:1403.2362

Dear Felix,

Thank you for your interest! Does the following answer your questions?

When discussing the equilibrium state associated with $R$, we are assuming that $R$ is quasiclassical. $\gamma_R$ has the form in your first set-off equation, $\gamma_R = e^{-\beta( H_R - \mu N_R)} / Z_R$. I

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Dear Nicole and Joe,

Very interesting paper! I have a question that is probably easily answered:

In the last paragraph of II.B, you state that each resource $R=(\rho,H,N)$ is associated with an equilibrium state $G = (g_R,H,N)$. I'm not sure how $g_R$ is defined. If $R=(\rho,H_R,N_R)$ and $S=(\sig

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