Special Issue "Machine Learning and Physics"

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (31 October 2021) | Viewed by 4834

Special Issue Editors

Prof. José D. Martín-Guerrero
E-Mail Website1 Website2
Guest Editor
Department of Electronic Engineering, School of Engineering (ETSE-UV), Universitat de València, 46100 Burjassot (Valencia), Spain
Interests: quantum machine learning; physics-inspired machine learning; reinforcement learning; deep learning; physics applications
Prof. Dr. Lucas Lamata
E-Mail Website
Guest Editor
Departamento de Física Atómica, Molecular y Nuclear, Universidad de Sevilla, 41080 Sevilla, Spain
Interests: quantum optics; quantum information; theoretical physics; quantum simulations; trapped ion physics; superconducting circuits; entanglement classification; entanglement generation; quantum biomimetics; artificial intelligence; machine learning; embedding quantum simulators; penning traps; quantum photonics
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Special Issue Information

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 2300 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.

Keywords

  • 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

Published Papers (3 papers)

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Research

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Article
Reconstruction of Ultra-High Vacuum Mass Spectra Using Genetic Algorithms
Appl. Sci. 2021, 11(24), 11754; https://doi.org/10.3390/app112411754 - 10 Dec 2021
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Abstract
In ultra-high vacuum systems, obtaining the composition of a mass spectrum is often a challenging task due to the highly overlapping nature of the individual profiles of the gas species that contribute to that spectrum, as well as the high differences in terms [...] Read more.
In ultra-high vacuum systems, obtaining the composition of a mass spectrum is often a challenging task due to the highly overlapping nature of the individual profiles of the gas species that contribute to that spectrum, as well as the high differences in terms of degree of contribution (several orders of magnitude). This problem is even more complex when not only the presence but also a quantitative estimation of the contribution (partial pressure) of each species is required. This paper aims at estimating the relative contribution of each species in a target mass spectrum by combining a state-of-the-art machine learning method (multilabel classifier) to obtain a pool of candidate species based on a threshold applied to the probability scores given by the classifier with a genetic algorithm that aims at finding the partial pressure at which each one of the species contributes to the target mass spectrum. For this purpose, we use a dataset of synthetically generated samples. We explore different acceptance thresholds for the generation of initial populations, and we establish comparative metrics against the most novel method to date for automatically obtaining partial pressure contributions. Our results show a clear advantage in terms of the integral error metric (up to 112 times lower for simpler spectra) and computational times (up to 4 times lower for complex spectra) in favor of the proposed method, which is considered a substantial improvement for this task. Full article
(This article belongs to the Special Issue Machine Learning and Physics)
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Article
How to Use Machine Learning to Improve the Discrimination between Signal and Background at Particle Colliders
Appl. Sci. 2021, 11(22), 11076; https://doi.org/10.3390/app112211076 - 22 Nov 2021
Cited by 1 | Viewed by 738
Abstract
The popularity of Machine Learning (ML) has been increasing in recent decades in almost every area, with the commercial and scientific fields being the most notorious ones. In particle physics, ML has been proven a useful resource to make the most of projects [...] Read more.
The popularity of Machine Learning (ML) has been increasing in recent decades in almost every area, with the commercial and scientific fields being the most notorious ones. In particle physics, ML has been proven a useful resource to make the most of projects such as the Large Hadron Collider (LHC). The main advantage provided by ML is a reduction in the time and effort required for the measurements carried out by experiments, and improvements in the performance. With this work we aim to encourage scientists working with particle colliders to use ML and to try the different alternatives that are available, focusing on the separation of signal and background. We assess some of the most-used libraries in the field, such as Toolkit for Multivariate Data Analysis with ROOT, and also newer and more sophisticated options such as PyTorch and Keras. We also assess the suitability of some of the most common algorithms for signal-background discrimination, such as Boosted Decision Trees, and propose the use of others, namely Neural Networks. We compare the overall performance of different algorithms and libraries in simulated LHC data and produce some guidelines to help analysts deal with different situations. Examples include the use of low or high-level features from particle detectors or the amount of statistics that are available for training the algorithms. Our main conclusion is that the algorithms and libraries used more frequently at LHC collaborations might not always be those that provide the best results for the classification of signal candidates, and fully connected Neural Networks trained with Keras can improve the performance scores in most of the cases we formulate. Full article
(This article belongs to the Special Issue Machine Learning and Physics)
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Review

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Review
Reinforcement Learning and Physics
Appl. Sci. 2021, 11(18), 8589; https://doi.org/10.3390/app11188589 - 16 Sep 2021
Cited by 3 | Viewed by 1582
Abstract
Machine learning techniques provide a remarkable tool for advancing scientific research, and this area has significantly grown in the past few years. In particular, reinforcement learning, an approach that maximizes a (long-term) reward by means of the actions taken by an agent in [...] Read more.
Machine learning techniques provide a remarkable tool for advancing scientific research, and this area has significantly grown in the past few years. In particular, reinforcement learning, an approach that maximizes a (long-term) reward by means of the actions taken by an agent in a given environment, can allow one for optimizing scientific discovery in a variety of fields such as physics, chemistry, and biology. Morover, physical systems, in particular quantum systems, may allow one for more efficient reinforcement learning protocols. In this review, we describe recent results in the field of reinforcement learning and physics. We include standard reinforcement learning techniques in the computer science community for enhancing physics research, as well as the more recent and emerging area of quantum reinforcement learning, inside quantum machine learning, for improving reinforcement learning computations. Full article
(This article belongs to the Special Issue Machine Learning and Physics)
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