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

Машинное обучение и искусственный интеллект в квантовой области: обзор последних достижений
Vedran Dunjko, Hans J. Briegel
2018-03-05

artificial intelligenceautonomous quantum experiment designquantum computingquantum machine learningquantum-enhanced learning
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.
1
Artificial intelligence has achieved initial successes in designing quantum experiments and autonomously performing parts of genuine scientific research.
2
Classical machine-learning methods are being applied to optimize quantum experiments and may become instrumental in advancing quantum technologies.
3
Quantum computing has shown potential to speed up machine-learning problems, particularly for large-scale data analysis.
4
Quantum machine learning investigates bidirectional benefits between quantum computing and machine learning, rather than treating the fields independently.
5
Research is also examining fundamental quantum generalizations of learning and intelligence concepts within a framework governed by quantum mechanics.
6
Theoretical results indicate quantum enhancements for interactive learning tasks, supporting the potential of quantum-enhanced learning agents.

Machine learning (ML) and artificial intelligence (AI) applied to and interacting with quantum information technologies and quantum computing

mutual enhancement, applications, and conceptual generalizations at the interface of quantum computing and machine learning/AI

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2018-03-05
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Vedran Dunjko
Hans J. Briegel
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