Machine learning \& artificial intelligence in the quantum domain

By Vedran Dunjko, Hans J. Briegel. arXiv.org, 2017

359 citations recorded by the source corpus.

Quantum information technologies, and intelligent learning systems, are both emergent technologies that will likely have a transforming impact on our society. The respective underlying fields of research -- quantum information (QI) versus machine learning (ML) and artificial intelligence (AI) -- have their own specific challenges, which have hitherto been investigated largely independently. However, in a growing body of recent work, researchers have been probing the question to what extent these fields can learn and benefit from each other. QML 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 breakthroughs in both directions of influence. For instance, quantum computing is finding a vital application in providing speed-ups in ML, critical in our "big data" world. Conversely, ML already permeates 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 demonstrated for interactive learning, 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, researchers have also broached the fundamental issue of quantum generalizations of ML/AI concepts. This deals with questions of the very meaning of learning and intelligence in a world that is described by quantum mechanics. In this review, we describe the main ideas, recent developments, and progress in a broad spectrum of research investigating machine learning and artificial intelligence in the quantum domain.

Subfields: Artificial Intelligence

View Paper: https://www.semanticscholar.org/paper/0cc6dbfd929bc816d507527993f55f9b4e88615d

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Citations in the corpus

2022 Roadmap on integrated quantum photonics (2021, methodology): “A more recent application of machine learning is for quantum photonic systems (figure 37), where techniques are being developed to solve Schrodinger’s equation rather than Maxwell’s equations [271].”
Experimental photonic quantum memristor (2021, background): “[7] Vedran Dunjko and Hans J Briegel, “Machine learning & artificial intelligence in the quantum domain: a review of recent progress,” Reports on Progress in Physics 81, 074001 (2018).”
A high-bias, low-variance introduction to Machine Learning for physicists (2018, neutral): “…al., 2018; August and Hernández-Lobato, 2018; Bukov, 2018; Bukov et al., 2018; Cárdenas-López et al., 2017; Chen et al., 2014; Chen and Xue, 2019; Dunjko et al., 2017; Fösel et al., 2018; Lamata, 2017; Melnikov et al., 2017; Neukart et al., 2017; Niu et al., 2018; Ramezanpour, 2017; Reddy et…”