基于自旋体系的量子机器学习实验进展

Experimental progress of quantum machine learning based on spin systems

机器学习因其在模式识别等问题上的优势已经被广泛应用到各个研究领域,然而其运算能力在一定程度上受到经典计算机算力的制约.近年来,随着量子技术的高速发展,量子计算加速的机器学习在诸多量子体系中进行了初步实验验证,并在某些特定问题上展示出了超越经典算法的优势.本文主要介绍两类典型的自旋体系-核磁共振体系和金刚石氮空位色心体系,并回顾近年来量子机器学习在这两类体系上的一些代表性实验工作.

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.