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

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.

The theory of the quantum kernel-based binary classifier

“…As shown in Eqs. (5) and (9), the qSVM and the HC encode the training and test data in the same qubit register, but separately in orthogonal subspaces of an ancilla qubit. Then the underlying principle of the classification is the interference of these subspaces.…”

Analytics and Big Data: Emerging trends and their impact on our lives

“…From being just a theoretical construct for physicists, quantum mechanics has come a long way. The modern day quantum computers designed using the principles of quantum systems can solve computational problems, which were so far believed to be intractable for classical computers (Dunjko & Briegel, 2018 (Dumitrescu et al, 2018). This demonstrates the capability of quantum computing to tackle nuclear physics problems.…”

Reconstruction of a Photonic Qubit State with Reinforcement Learning

“…Machine learning (ML), as a subtopic within artificial intelligence (AI) realm, has already become a powerful tool for data mining, pattern recognition, among others. Meanwhile, there are many recent works combining ML techniques with quantum information tools . These include expressing and witnessing quantum entanglement by artificial neural networks (ANN), analyzing and restructuring a quantum state by restricted Boltzmann machines (RBM), as well as detecting quantum change points, and learning Hamiltonians by Bayesian inference .…”