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In the past few days, I have been trying to collect material (mostly research papers) related to Quantum machine learning and its applications, for a summer project. Here are a few which I found interesting (from a superficial reading):

- Unsupervised Machine Learning on a Hybrid Quantum Computer (J.S. Otterbach et al., 2017)
- Quantum algorithms for supervised and unsupervised machine learning (Lloyd, Mohseni & Rebentrost, 2013)
- A Machine Learning Framework to Forecast Wave Conditions (James, Zhang & O'Donncha 2017)
- Quantum Neuron: an elementary building block for machine learning on quantum computers (Cao, Guerreschi & Aspuru-Guzik, 2017)
- Quantum machine learning for quantum anomaly detection (Liu & Rebentrost, 2017)

However, coming from the more *physics-y* end of the spectrum, I don't have much
background knowledge in this area and am finding most of the specialized materials impenetrable. **Ciliberto et al.**'s paper: Quantum machine learning: a classical perspective somewhat helped me to grasp some of the basic concepts. I'm looking for similar but **more elaborate** introductory material. It would be very helpful if you could recommend textbooks, video lectures, etc. which provide a good introduction to the field of quantum machine learning.

For instance, Nielsen and Chuang's textbook is a great introduction to the quantum computing and quantum algorithms in general and goes quite far in terms of introductory material (although it begins at a very basic level and covers all the necessary portions of quantum mechanics and linear algebra and even the basics of computational complexity!). Is there anything similar for quantum machine learning?

**P.S:** I do realize that quantum machine learning is a vast area. In case there is any confusion, I would like to point out that I'm mainly looking for textbooks/introductory papers/lectures which cover the details of the quantum analogues of classical machine learning algorithms.

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This looks good. Here's another review paper from 2014, which I found useful: arXiv:1409:3097.

– Sanchayan Dutta – 2018-05-27T07:16:53.503Yes, a bit older but also great. I know all three authors and do endorse their work. Keep in mind "quantum machine learning" is still a new topic, and many of the authors of the Nature paper have said that most of the time spent on that paper was on arguing over what the field even is. Therefore it's a bit early for there to be a perfect introduction like Nielsen and Chuang is for quantum computing, but the Nature paper, combined with the paper you suggested, is probably the best. – user1271772 – 2018-05-27T07:30:17.870

7this is definitely

nota "Nielsen and Chuang" of QML. It is a review paper and as such not much more than a list of references, with a few words attached, to what has been and is being done in the field (not that this is bad in any way: the paper perfectly achieves its purpose). I would say that Wittek's book on quantum machine learning is a better fit for such a title, but really the field is not mature enough yet to have anything equivalent to a "N&C of QML" – glS – 2018-05-29T17:28:07.5732This review has just about 14 pages in single column. This is hardly an "extensive review" (e.g., Rev. Mod. Phys. papers have about 40 pages in 2-column). Not to mention that it cannot be compared to a book like Nielsen and Chuang with its about 600 pages. – Norbert Schuch – 2018-11-24T21:52:16.213

2This review paper over-emphasizes use of oracle-based algorithms like HHL, which is understandable given the author list but hardly representative of the field. – forky40 – 2019-05-30T19:07:17.267