Experimental Quantum‐Enhanced Machine Learning in Spin‐Based Systems. Issue 8 (11th June 2022)

• Record Type: Journal Article
• Authors: Wang, Xiangyu; Lin, Zidong; Che, Liangyu; Chen, Hanyu; Lu, Dawei
• Is Part Of: Advanced quantum technologies. Volume 5:Issue 8(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-06-11
• Subjects: nitrogen‐vacancy centers -- nuclear magnetic resonance -- quantum machine learning -- spin qubits; Quantum theory -- Periodicals; Quantum computing -- Periodicals; Quantum chemistry -- Periodicals; Quantum electronics -- Periodicals; 537.5
• DOI: 10.1002/qute.202200005
• Languages: English
• ISSNs: 2511-9044

Abstract

Abstract: With the advancement of computing power and algorithms, machine learning has been a powerful tool in numerous applications nowadays. However, the hardware limitation of classical computers and the increasing size of datasets urge the community to explore new techniques for machine learning. Quantum‐enhanced machine learning is such a rapidly growing field. It refers to quantum algorithms that are implemented in quantum computers, which can improve the computational speed of classical machine learning tasks and often promises an exponential speedup. In the past few years, the development of experimental quantum technologies leads to many experimental demonstrations of quantum‐enhanced machine learning in diverse physical systems. Here, the recent experimental progress in this field in two typical spin‐based quantum systems—nuclear magnetic resonance and nitrogen‐vacancy centers in diamond—is reviewed, and the ongoing challenges are discussed. Abstract : Quantum‐enhance machine learning (QEML) refers to quantum algorithms that are implemented in quantum computers, which can improve the computational speed of classical machine learning tasks and often promises an exponential speedup. This review summarizes recent progress in the domain of experimental QEML using spin‐based quantum systems, and discusses the ongoing challenges.