标题： 用于光子量子计算机的状态制备和门合成的机器学习方法 显示英文标题

标题： Machine learning method for state preparation and gate synthesis on photonic quantum computers

摘要： 我们展示了如何利用机器学习和优化技术来找到执行输入状态和输出状态之间期望变换的光子量子计算机电路。 在最简单的情况下，即单个输入状态，我们的方法可以发现用于制备期望量子态的电路。 在更一般的情况下，即多个输入和输出关系，我们的方法可以获得能够再现目标单位变换作用的电路。 我们使用连续变量量子神经网络作为电路架构。 该网络由多个具有可变参数的光学门层组成，这些参数通过应用自动微分进行优化，使用Strawberry Fields光子量子计算机模拟器的TensorFlow后端。 我们通过学习如何使用短深度电路来合成单光子、Gottesman-Kitaev-Preskill态、NOON态、立方相位门、随机单位变换、交叉-Kerr相互作用以及几种其他态和门来展示我们方法的强大和多样性。 我们通常使用短深度电路获得高于99%的保真度，这些电路通常由几百个门组成。 只需指定目标态或门并运行优化算法，即可自动获得电路。 显示英文摘要

摘要： We show how techniques from machine learning and optimization can be used to find circuits of photonic quantum computers that perform a desired transformation between input and output states. In the simplest case of a single input state, our method discovers circuits for preparing a desired quantum state. In the more general case of several input and output relations, our method obtains circuits that reproduce the action of a target unitary transformation. We use a continuous-variable quantum neural network as the circuit architecture. The network is composed of several layers of optical gates with variable parameters that are optimized by applying automatic differentiation using the TensorFlow backend of the Strawberry Fields photonic quantum computer simulator. We demonstrate the power and versatility of our methods by learning how to use short-depth circuits to synthesize single photons, Gottesman-Kitaev-Preskill states, NOON states, cubic phase gates, random unitaries, cross-Kerr interactions, as well as several other states and gates. We routinely obtain high fidelities above 99\% using short-depth circuits, typically consisting of a few hundred gates. The circuits are obtained automatically by simply specifying the target state or gate and running the optimization algorithm.