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Acta Physica Sinica

Preface to the special topic: Machine learning and physics

Published Jan 1, 2021• 0 citations
Publisher

Abstract

Machine learning, especially deep learning, has achieved remarkable success in a wide range of applications.It is one of today' s most rapidly growing fields in science and technology.In recent years, the interplay between machine learning and physics has attracted tremendous attention, giving rise to a new interdisciplinary research frontier.On the one hand, we may utilize machine learning methods to tackle certain intricate physical problems that are beyond the capability of traditional approaches.On the other hand, certain concepts, ideas, and methods originated in physics can also be exploited to enhance the study of machine learning.Without a doubt, the fusion of machine learning and physics will bring us new opportunities and challenges, and significantly advance the studies in both fields.This special topic contains several review papers written by experts working actively in this emergent interdisciplinary field.These papers review a number of hot topics and some latest progresses, covering adversarial learning in quantum artificial intelligence, quantum generative models, machine learning based on waves and diffusions, automatic differentiation, machine learning assisted quantum adiabatic algorithm design, state preparation in quantum machine learning, experimental progress of quantum machine learning based on spin systems, etc.We hope this special topic can help readers gain a primary picture of the research content, basic ideas and methods, the latest developments, and the challenges and opportunities faced in the intersection of machine learning and physics.Meanwhile, we also hope this special topic can provide some inspiration to readers, and attract more researchers to join this exciting interdisciplinary field.

DOI
10.7498/aps.70.140101

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Machine learning, especially deep learning, has achieved remarkable success in a wide range of applications.It is one of today' s most rapidly growing fields in science and technology.In recent years, the interplay between machine learning and physics has attracted tremendous attention, giving rise to a new interdisciplinary research frontier.On the one hand, we may utilize machine learning methods to tackle certain intricate physical problems that are beyond the capability of traditional approaches.On the other hand, certain concepts, ideas, and methods originated in physics can also be exploited to enhance the study of machine learning.Without a doubt, the fusion of machine learning and physics will bring us new opportunities and challenges, and significantly advance the studies in both fields.This special topic contains several review papers written by experts working actively in this emergent interdisciplinary field.These papers review a number of hot topics and some latest progresses, covering adversarial learning in quantum artificial intelligence, quantum generative models, machine learning based on waves and diffusions, automatic differentiation, machine learning assisted quantum adiabatic algorithm design, state preparation in quantum machine learning, experimental progress of quantum machine learning based on spin systems, etc.We hope this special topic can help readers gain a primary picture of the research content, basic ideas and methods, the latest developments, and the challenges and opportunities faced in the intersection of machine learning and physics.Meanwhile, we also hope this special topic can provide some inspiration to readers, and attract more researchers to join this exciting interdisciplinary field.
Quantum information technologies, on the one hand, and intelligent learning systems, on the other, are both emergent technologies that are likely to have a transformative impact on our society in the future. The respective underlying fields of basic research-quantum information versus machine learning (ML) and artificial intelligence (AI)-have their own specific questions and challenges, which have hitherto been investigated largely independently. However, in a growing body of recent work, researchers have been probing the question of the extent to which these fields can indeed learn and benefit from each other. Quantum ML explores the interaction between quantum computing and ML, investigating how results and techniques from one field can be used to solve the problems of the other. Recently we have witnessed significant breakthroughs in both directions of influence. For instance, quantum computing is finding a vital application in providing speed-ups for ML problems, critical in our 'big data' world. Conversely, ML already permeates many cutting-edge technologies and may become instrumental in advanced quantum technologies. Aside from quantum speed-up in data analysis, or classical ML optimization used in quantum experiments, quantum enhancements have also been (theoretically) demonstrated for interactive learning tasks, highlighting the potential of quantum-enhanced learning agents. Finally, works exploring the use of AI for the very design of quantum experiments and for performing parts of genuine research autonomously, have reported their first successes. Beyond the topics of mutual enhancement-exploring what ML/AI can do for quantum physics and vice versa-researchers have also broached the fundamental issue of quantum generalizations of learning and AI concepts. This deals with questions of the very meaning of learning and intelligence in a world that is fully described by quantum mechanics. In this review, we describe the main ideas, recent developments and progress in a broad spectrum of research investigating ML and AI in the quantum domain.
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Machine learning is a branch of artificial intelligence, and it has been widely used in many science and engineering areas, such as data mining, natural language processing, computer vision, biological analysis, and so on. Quantum computer is considered as one of the most promising technologies of human beings in the near future. With the development of machine learning and quantum computing, researchers consider to combine these two aspects to gain more benefits. As a result, a novel interdisciplinary subject has emerged—quantum machine learning. This article reviews the state-of-the-art research of algorithms of quantum machine learning and shows a path of the research from the basic quantum information to quantum machine learning algorithms from the perspective of people in the field of computer science.
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