Experimental realization of a quantum image classifier via tensor-network-based machine learning

作     者：KUNKUN WANG LEI XIAO WEI YI SHI-JU RAN PENG XUE 

作者机构：Beijing Computational Science Research CenterBeijing 100084China School of Physics and Optoelectronics EngineeringAnhui UniversityHefei 230601China CAS Key Laboratory of Quantum InformationUniversity of Science and Technology of ChinaHefei 230026China CAS Center for Excellence in Quantum Information and Quantum PhysicsHefei 230026China Department of PhysicsCapital Normal UniversityBeijing 100048China 

出 版 物：《Photonics Research》 (光子学研究（英文版）)

年 卷 期：2021年第9卷第12期

页      面：2332-2340页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 07[理学] 081104[工学-模式识别与智能系统] 08[工学] 080203[工学-机械设计及理论] 070201[理学-理论物理] 0835[工学-软件工程] 0802[工学-机械工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 0702[理学-物理学] 

基　　金：National Natural Science Foundation of China(12025401,11674189,U1930402,11974331,11834014) Project Funded by China Postdoctoral Science Foundation(2019M660016,2020M680006) National Key Research and Development Program of China(2016YFA0301700,2017YFA0304100) Beijing Natural Science Foundation(1192005,Z180013) Academy for Multidisciplinary Studies,Capital Normal University 

主　　题：tensor quantum realization 

摘      要：Quantum machine learning aspires to overcome intractability that currently limits its applicability to practical ***,quantum machine learning itself is limited by low effective dimensions achievable in stateof-the-art ***,we demonstrate highly successful classifications of real-life images using photonic qubits,combining a quantum tensor-network representation of hand-written digits and entanglement-based ***,we focus on binary classification for hand-written zeroes and ones,whose features are cast into the tensor-network representation,further reduced by optimization based on entanglement entropy and encoded into two-qubit photonic *** then demonstrate image classification with a high success rate exceeding 98%,through successive gate operations and projective *** we work with photons,our approach is amenable to other physical realizations such as nitrogen-vacancy centers,nuclear spins,and trapped ions,and our scheme can be scaled to efficient multi-qubit encodings of features in the tensor-product representation,thereby setting the stage for quantum-enhanced multi-class classification.