3 years ago

Experimental quantum stochastic walks simulating associative memory of Hopfield neural networks.

Hao Tang, Zhen Feng, Ying-han Wang, Peng-cheng Lai, Chao-yue Wang, Zhuo-yang Ye, Cheng-kai Wang, Zi-yu Shi, Tian-yu Wang, Yuan Chen, Jun Gao, Xian-min Jin

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 for easier mapping to the quantum regime. Quantum stochastic walk, a strikingly powerful tool to analyze quantum dynamics, has been recently proposed to simulate the firing pattern and associative memory with a dependence on Hamming Distance. We successfully map the theoretical scheme into a three-dimensional photonic 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 towards photonic artificial intelligence devices for optimization and computation tasks of greatly improved efficiencies.

Publisher URL: http://arxiv.org/abs/1901.02462

DOI: arXiv:1901.02462v1

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