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

## TL;DR

This paper demonstrates a photonic quantum simulation of associative memory in Hopfield neural networks using quantum stochastic walks on a 3D photonic chip, showing promising scalability and efficiency improvements.

## Contribution

It introduces a novel implementation of quantum stochastic walks on a photonic chip to simulate neural network associative memory, bridging quantum simulation and neural network models.

## Key findings

- High match rate between experimental and theoretical associative memory results
- Successful mapping of neural network dynamics onto a photonic quantum chip
- Scalable approach with low-loss integrated photonic technology

## Abstract

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 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 towards photonic artificial intelligence devices for optimization and computation tasks of greatly improved efficiencies.