标题： 实验量子随机游走模拟霍普菲尔德神经网络的联想记忆 显示英文标题

标题： Experimental quantum stochastic walks simulating associative memory of Hopfield neural networks

摘要： 随着量子信息和机器学习之间的交叉日益增多，神经网络的量子模拟引起了前所未有的广泛关注，特别是由于其在Hopfield神经网络中的联想记忆应用广泛，且结构相对简单，使其更容易映射到量子领域。量子随机行走是一种分析量子动力学的有力工具，最近被提出用于模拟依赖于汉明距离的放电模式和联想记忆。我们成功地将理论方案映射到一个三维光子量子芯片，并通过传播常数的精确调控实现了量子随机行走演化。我们展示了实验量子方案与预期结果之间在Hopfield神经网络联想记忆中的良好匹配率。量子模拟神经网络一个重要特征的能力，结合我们通过低损耗集成芯片和直接哈密顿量工程实现的可扩展性，为具有更高效率的光子人工智能设备在优化和计算任务方面提供了初步但稳定的一步。 显示英文摘要

摘要： 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.