Machine Learning and Physics

Dear Colleagues,

We invite you to contribute to a Special Issue of the journal Applied Sciences on “Machine Learning and Physics”. Machine learning (ML) has become extremely popular due to successful results in many different applications. Those results are sometimes produced by well-known methods; nonetheless, the advent of new and disruptive approaches is behind many outcomes that were unthinkable just a few years ago, some deep learning contributions being a paradigmatic example, especially with the proposal of new convolutional, generative, and recurrent networks.

A disruptive field of research that has gained relevance recently comes from physics, where quantum machine learning (QML) is already providing calculation speed-ups while not worsening the performance in some controlled problems. Some classical ML approaches find their quantum counterparts, such as neural networks or reinforcement learning (RL); RL has also demonstrated its usefulness to control quantum experiments; other ML paradigms, such as active learning, have shown their suitability to reducing measuring in quantum experimentation.

The relationship between ML and physics also encompasses physics-inspired ML algorithms, which are a natural solution for some physics applications but also an alternative representation that can provide different—and oftentimes better—solutions to problems from other fields, as shown by quantum clustering, for example.

The pure application of classical ML models to physics problems must not be cast aside, either. There are many problems in physics that involve huge amounts of data to be modeled, hence representing an ideal scenario for ML. The relevance of ML in quantum metrology also deserves a profound analysis.

Therefore, there is plenty of research to be carried out in this fuzzy border between ML and physics, and we truly reckon that this Special Issue might be an ideal channel to disseminate it. We thus invite you to submit your contributions on the field specified (but not restricted) by the keywords, in the form of original research papers, mini-reviews, and perspective articles.

Prof. José D. Martín Guerrero
Prof. Dr. Lucas Lamata
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• information theory
• learning in quantum environments
• ml applied to problems in physics
• physics-inspired ml algorithms
• reinforcement learning for the control of physical systems
• semi-supervised approaches for quantum metrology
• quantum annealing
• quantum computing
• quantum clustering
• quantum principal component analysis
• quantum support vector machines
• quantum neural networks
• quantum regression
• quantum technologies