Experimental Quantum Stochastic Walks Simulating Associative Memory of Hopfield Neural Networks

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.