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Physical Review A

Experimental demonstration of quantum-enhanced machine learning in a nitrogen-vacancy-center system

Xiaolong Ouyang, Xianzhi Huang, Yukai Wu, W.-G. Zhang, Xunuo Wang, Huanlong Zhang, L. He, X.-Y. Chang, L.-M. Duan

Published Jan 9, 2020• 15 citations
Publisher

Abstract

We demonstrate the quantum-enhanced supervised classification of vectors in a nitrogen-vacancy ($NV$)--center system using the algorithm of Lloyd, Mohseni, and Rebentrost (arXiv:1307.0411). A $^{13}\mathrm{C}$ nuclear spin is employed to encode the vectors with the spin-triplet state of the $NV$ center as an ancilla. We design efficient methods to prepare the initial electron-nuclear entangled state within the coherence time ${T}_{2}^{*}$ of the electron spin, and then compute the distance between the test vector and the center of each class by measuring the level population through maximum likelihood estimation. Our experiment allows more than one reference vector in each class, thus forming an important enabling step toward quantum-enhanced machine learning.

DOI
10.1103/physreva.101.012307

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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.
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Machine learning is widely applied in various areas due to its advantages in pattern recognition, but it is severely restricted by the computing power of classic computers. In recent years, with the rapid development of quantum technology, quantum machine learning has been verified experimentally verified in many quantum systems, and exhibited great advantages over classical algorithms for certain specific problems. In the present review, we mainly introduce two typical spin systems, nuclear magnetic resonance and nitrogen-vacancy centers in diamond, and review some representative experiments in the field of quantum machine learning, which were carried out in recent years.