Physically motivated quantum algorithms for specific near-term quantum hardware will likely be the next frontier in quantum information science. Here, we show how many of the features of neural networks for machine learning can naturally be mapped into the quantum optical domain by introducing the quantum optical neural network (QONN). Through numerical simulation and analysis we train the QONN to perform a range of quantum information processing tasks, including newly developed protocols for quantum optical state compression, reinforcement learning, black-box quantum simulation, and one-way quantum repeaters. We consistently demonstrate that our system can generalize from only a small set of training data onto inputs for which it has not been trained. Our results indicate that QONNs are a powerful design tool for quantum optical systems and, leveraging advances in integrated quantum photonics, a promising architecture for next-generation quantum processors.

用于特定近期量子硬件的物理量子算法可能会成为量子信息科学的下一个前沿领域。在这里，我们介绍了通过引入量子光学神经网络（QONN），自然可以将用于机器学习的神经网络的许多功能映射到量子光学域中。通过数值模拟和分析，我们训练QONN执行一系列量子信息处理任务，包括新开发的用于量子光学状态压缩，增强学习，黑盒量子模拟和单向量子中继器的协议。我们始终如一地证明，我们的系统可以从一小部分训练数据推广到尚未对其进行训练的输入。我们的结果表明，QONN是用于量子光学系统的强大设计工具，并且，