Literature DB >> 29504942

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

Vedran Dunjko1, Hans J Briegel.   

Abstract

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.

Entities:  

Year:  2018        PMID: 29504942     DOI: 10.1088/1361-6633/aab406

Source DB:  PubMed          Journal:  Rep Prog Phys        ISSN: 0034-4885


  16 in total

1.  The machine learning approach: Artificial intelligence is coming to support critical clinical thinking.

Authors:  Carmela Nappi; Alberto Cuocolo
Journal:  J Nucl Cardiol       Date:  2018-06-19       Impact factor: 5.952

2.  Predicting research trends with semantic and neural networks with an application in quantum physics.

Authors:  Mario Krenn; Anton Zeilinger
Journal:  Proc Natl Acad Sci U S A       Date:  2020-01-14       Impact factor: 11.205

3.  Quantum Artificial Life in an IBM Quantum Computer.

Authors:  U Alvarez-Rodriguez; M Sanz; L Lamata; E Solano
Journal:  Sci Rep       Date:  2018-10-04       Impact factor: 4.379

4.  Multiqubit and multilevel quantum reinforcement learning with quantum technologies.

Authors:  F A Cárdenas-López; L Lamata; J C Retamal; E Solano
Journal:  PLoS One       Date:  2018-07-19       Impact factor: 3.240

5.  On the convergence of projective-simulation-based reinforcement learning in Markov decision processes.

Authors:  W L Boyajian; J Clausen; L M Trenkwalder; V Dunjko; H J Briegel
Journal:  Quantum Mach Intell       Date:  2020-11-05

6.  A Novel Autonomous Perceptron Model for Pattern Classification Applications.

Authors:  Alaa Sagheer; Mohammed Zidan; Mohammed M Abdelsamea
Journal:  Entropy (Basel)       Date:  2019-08-06       Impact factor: 2.524

Review 7.  Machine learning toward advanced energy storage devices and systems.

Authors:  Tianhan Gao; Wei Lu
Journal:  iScience       Date:  2020-12-13

8.  Speeding up quantum perceptron via shortcuts to adiabaticity.

Authors:  Yue Ban; Xi Chen; E Torrontegui; E Solano; J Casanova
Journal:  Sci Rep       Date:  2021-03-11       Impact factor: 4.379

9.  Emerging Complexity in Distributed Intelligent Systems.

Authors:  Valentina Guleva; Egor Shikov; Klavdiya Bochenina; Sergey Kovalchuk; Alexander Alodjants; Alexander Boukhanovsky
Journal:  Entropy (Basel)       Date:  2020-12-19       Impact factor: 2.524

Review 10.  Nanosystems, Edge Computing, and the Next Generation Computing Systems.

Authors:  Ali Passian; Neena Imam
Journal:  Sensors (Basel)       Date:  2019-09-19       Impact factor: 3.576

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