Experimental Quantum Stochastic Walks Simulating Associative Memory of Hopfield Neural Networks

Tang, Hao1,2,3; Feng, Zhen1,3; Wang, Ying-Han1,4; Lai, Peng-Cheng1,4; Wang, Chao-Yue1,4; Ye, Zhuo-Yang1,4; Wang, Cheng-Kai1,4; Shi, Zi-Yu1,3; Wang, Tian-Yu1; Chen, Yuan1,3,5,6; Gao, Jun1,3,5,6; Jin, Xian-Min1,2,3

2019-02-07

10.1103/PhysRevApplied.11.024020

Physical Review Applied   影响因子和分区

2331-7019

With the increasing crossover between quantum information and machine learning, quantum simulation of neural networks has drawn unprecedentedly strong attention, especially for the simulation of associative memory in Hopfield neural networks due to their wide applications and relatively simple structures that allow easier mapping to the quantum regime. Quantum stochastic walk, a strikingly powerful tool to analyze quantum dynamics, has recently been proposed to simulate the firing pattern and associative memory with a dependence on the Hamming distance. We successfully map the theoretical scheme into a three-dimensional photonic quantum chip and realize quantum-stochastic-walk evolution through well-controlled detunings of the propagation constant. We demonstrate a good match rate of the associative memory between the experimental quantum scheme and the expected result for Hopfield neural networks. The ability of quantum simulation for an important feature of a neural network combined with the scalability of our approach through low-loss-integrated-chip and straightforward Hamiltonian engineering provides a primary but steady step toward photonic artificial-intelligence devices for optimization and computation tasks with greatly increased efficiencies.

SCI ; EI

英语

其他

Zhiyuan Scholar Program[ZIRC2016-01] ; Zhiyuan Scholar Program[ZIRC2017-05]

Physics

Physics, Applied

WOS:000458156200004

AMER PHYSICAL SOC

20191306695274

Associative processing ; Associative storage ; Hamming distance ; Learning systems ; Memory architecture ; Quantum chemistry ; Quantum optics ; Stochastic systems

Computer Systems and Equipment:722 ; Data Storage, Equipment and Techniques:722.1 ; Data Processing and Image Processing:723.2 ; Light/Optics:741.1 ; Physical Chemistry:801.4 ; Systems Science:961

Web of Science

1.Shanghai Jiao Tong Univ, Sch Phys & Astron, Shanghai 200240, Peoples R China
2.Shanghai Jiao Tong Univ, Inst Nat Sci, Shanghai 200240, Peoples R China
3.Univ Sci & Technol China, Synerget Innovat Ctr Quantum Informat & Quantum P, Hefei 230026, Anhui, Peoples R China
4.Shanghai Jiao Tong Univ, Zhiyuan Innovat Res Ctr, Shanghai 200240, Peoples R China
5.Southern Univ Sci & Technol, Inst Quantum Sci & Engn, Shenzhen 518055, Peoples R China
6.Southern Univ Sci & Technol, Dept Phys, Shenzhen 518055, Peoples R China

Tang, Hao,Feng, Zhen,Wang, Ying-Han,et al. Experimental Quantum Stochastic Walks Simulating Associative Memory of Hopfield Neural Networks[J]. Physical Review Applied,2019,11(2).

Tang, Hao.,Feng, Zhen.,Wang, Ying-Han.,Lai, Peng-Cheng.,Wang, Chao-Yue.,...&Jin, Xian-Min.(2019).Experimental Quantum Stochastic Walks Simulating Associative Memory of Hopfield Neural Networks.Physical Review Applied,11(2).

Tang, Hao,et al."Experimental Quantum Stochastic Walks Simulating Associative Memory of Hopfield Neural Networks".Physical Review Applied 11.2(2019).