  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, and black-box quantum simulation. We consistently
demonstrate our system can generalize from only a small set of training data
onto states for which it has not been trained. Our results indicate 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.
