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With the increasing crossover between quantum information and machinelearning, quantum simulation of neural networks has drawn unprecedentedlystrong attention, especially for the simulation of associative memory inHopfield neural networks due to their wide applications and relatively simplestructures that allow for easier mapping to the quantum regime. Quantumstochastic walk, a strikingly powerful tool to analyze quantum dynamics, hasbeen recently proposed to simulate the firing pattern and associative memorywith a dependence on Hamming Distance. We successfully map the theoreticalscheme into a three-dimensional photonic quantum chip and realize quantumstochastic walk evolution through well-controlled detunings of the propagationconstant. We demonstrate a good match rate of the associative memory betweenthe experimental quantum scheme and the expected result for Hopfield neuralnetworks. The ability of quantum simulation for an important feature of aneural network, combined with the scalability of our approach through low-lossintegrated chip and straightforward Hamiltonian engineering, provides a primarybut steady step towards photonic artificial intelligence devices foroptimization and computation tasks of greatly improved efficiencies.

Experimental quantum stochastic walks simulating associative memory of Hopfield neural networks