Quantum optical neural networks for next generation quantum information processing

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 protocols for quantum optical state compression, reinforcement learning, and black-box quantum simulation. Our results indicate QONNs are a powerful design tool for quantum optical systems and a promising architecture for next generation quantum processors.