<p>The survey <a href="https://arxiv.org/abs/1511.04206" rel="nofollow noreferrer">https://arxiv.org/abs/1511.04206</a> by Montanaro is very nice in terms of giving a bird's eye view, which is very useful. As the author states in the abstract</p>
<blockquote>
<p>Here we briefly survey some known quantum algorithms, with an emphasis on a broad overview of their applications rather than their technical details. We include a discussion of recent developments and near-term applications of quantum algorithms.</p>
</blockquote>
<p>That survey is now 7 years old and the field has changed. Not being an expert but just an interested outsider, I was wondering if there is a more up to date survey with similar scope and detail.</p>
<p><strong>Edit:</strong></p>
<p>Here are three more recent surveys I found, only the first one is general.</p>
<p>Quantum Computing 2022, by James D. Whitfield et al
<a href="https://arxiv.org/pdf/2201.09877.pdf" rel="nofollow noreferrer">https://arxiv.org/pdf/2201.09877.pdf</a></p>
<blockquote>
<p>[We aim to] provide a sober assessment of the promise of quantum
technology with a focus on computing. We provide a tour of
quantum computing and quantum technology that is aimed to
be comprehensible to scientists and engineers without becoming
a popular account. The goal is not a comprehensive review nor
a superficial introduction but rather to serve as a useful map to
navigate the hype, the scientific literature, and upcoming press
releases about quantum technology and quantum computing.</p>
</blockquote>
<p>Ciliberto C, et al.
Quantum machine learning: a
classical perspective. Proceedings of the Royal Society A, 2018.</p>
<p><a href="https://royalsocietypublishing.org/doi/pdf/10.1098/rspa.2017.0551" rel="nofollow noreferrer">https://royalsocietypublishing.org/doi/pdf/10.1098/rspa.2017.0551</a></p>
<blockquote>
<p>Here we review the literature in quantum
ML and discuss perspectives for a mixed readership
of classical ML and quantum computation experts.
Particular emphasis will be placed on clarifying
the limitations of quantum algorithms, how they
compare with their best classical counterparts and
why quantum resources are expected to provide
advantages for learning problems</p>
</blockquote>
<p>Laszlo Gyongyosi, Sandor Imre, A Survey on quantum computing technology,
<em>Computer Science Review</em>, 2019.</p>
<p><a href="https://doi.org/10.1016/j.cosrev.2018.11.002" rel="nofollow noreferrer">https://doi.org/10.1016/j.cosrev.2018.11.002</a></p>
<blockquote>
<p>The power of quantum computing technologies is based on the fundamentals of quantum mechanics,
such as quantum superposition, quantum entanglement, or the no-cloning theorem. Since
these phenomena have no classical analogue, similar results cannot be achieved within
the framework of traditional computing. The experimental insights of quantum computing
technologies have already been demonstrated, and several studies are in progress.
Here we review the most recent results of quantum computation technology and
address the open problems of the field.</p>
</blockquote>
<p>I would appreciate any comments on these references and any suggestions of more surveys to look at.</p>

CC BY-SA 4.0
2022-06-12T05:21:06.080Z
Have you tried looking at all more recent papers that cite it, using e.g. Google Scholar?
D.W.
CC BY-SA 4.0
2022-07-15T19:55:19.450Z
Do you know about the [Quantum Algorithm Zoo](https://quantumalgorithmzoo.org/)? I believe it's actively maintained, the last update was on February 1, 2021.
lamontap
<ds.algorithms><quantum-computing>
Survey of Quantum Algorithms similar to Montanaro's from 2015
kodlu
# Survey of Quantum Algorithms similar to Montanaro's from 2015

(Asked by: username_0 on 2022-06-10)

The survey <https://arxiv.org/abs/1511.04206> by Montanaro is very nice in terms of giving a bird's eye view, which is very useful. As the author states in the abstract

> Here we briefly survey some known quantum algorithms, with an emphasis on a broad overview of their applications rather than their technical details. We include a discussion of recent developments and near-term applications of quantum algorithms.

That survey is now 7 years old and the field has changed. Not being an expert but just an interested outsider, I was wondering if there is a more up to date survey with similar scope and detail.

**Edit:**

Here are three more recent surveys I found, only the first one is general.

Quantum Computing 2022, by James D. Whitfield et al
<https://arxiv.org/pdf/2201.09877.pdf>

> [We aim to] provide a sober assessment of the promise of quantum
> technology with a focus on computing. We provide a tour of
> quantum computing and quantum technology that is aimed to
> be comprehensible to scientists and engineers without becoming
> a popular account. The goal is not a comprehensive review nor
> a superficial introduction but rather to serve as a useful map to
> navigate the hype, the scientific literature, and upcoming press
> releases about quantum technology and quantum computing.

Ciliberto C, et al.
Quantum machine learning: a
classical perspective. Proceedings of the Royal Society A, 2018.

<https://royalsocietypublishing.org/doi/pdf/10.1098/rspa.2017.0551>

> Here we review the literature in quantum
> ML and discuss perspectives for a mixed readership
> of classical ML and quantum computation experts.
> Particular emphasis will be placed on clarifying
> the limitations of quantum algorithms, how they
> compare with their best classical counterparts and
> why quantum resources are expected to provide
> advantages for learning problems

Laszlo Gyongyosi, Sandor Imre, A Survey on quantum computing technology,
*Computer Science Review*, 2019.

<https://doi.org/10.1016/j.cosrev.2018.11.002>

> The power of quantum computing technologies is based on the fundamentals of quantum mechanics,
> such as quantum superposition, quantum entanglement, or the no-cloning theorem. Since
> these phenomena have no classical analogue, similar results cannot be achieved within
> the framework of traditional computing. The experimental insights of quantum computing
> technologies have already been demonstrated, and several studies are in progress.
> Here we review the most recent results of quantum computation technology and
> address the open problems of the field.

I would appreciate any comments on these references and any suggestions of more surveys to look at.

**Tags:** ds.algorithms, quantum-computing

> **Comments:**
> **username_1**: Have you tried looking at all more recent papers that cite it, using e.g. Google Scholar?  
> **username_2**: Do you know about the [Quantum Algorithm Zoo](https://quantumalgorithmzoo.org/)? I believe it's actively maintained, the last update was on February 1, 2021.  

---

## 0 answers

