Machine learning & artificial intelligence in the quantum domain: a review of recent progress

By Vedran Dunjko, Hans J. Briegel. Reports on progress in physics. Physical Society, 2018

684 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Atomic and Molecular Physics, and Optics

View Paper: https://www.semanticscholar.org/paper/ca83aba458c3d42d5b47636c3272943f06f01a48

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Citations in the corpus

Discovering physical concepts with neural networks (2018, background): “1 and [10] for a review and further references).”
Quantum Fisher information matrix and multiparameter estimation (2019, methodology): “In recent years, it has been applied to condensed matter physics for phase transitions [194, 195], design of a magneto-optical trap [196] and other aspects [197].”
Artificial Intelligence and Entrepreneurship: Implications for Venture Creation in the Fourth Industrial Revolution (2020, background): “…disruption ( Schwab, 2017 ); it is predicted that AI will diffuse rapidly ( Taddy, 2018), driven by innovations in both machine learning techniques and the AI technology stack which encompasses a new generation of “intelligent” processors and quantum computers (Dunjko & Briegel, 2018).”
AI for Next Generation Computing: Emerging Trends and Future Directions (2022, neutral): “Constraint resolution, uncertainty handling, and constraint fulfilment will all be improved by quantum computing, as will daptive machine learning and spatial and temporal reasoning [178].”
Variational Quantum Circuits for Deep Reinforcement Learning (2019, background): “A general review of the field of quantum machine learning can refer to [46], [47].”