Quantum optical neural networks

TL;DR

This paper introduces the quantum optical neural network (QONN), a novel architecture that maps neural network features into quantum optical systems, demonstrating its potential for quantum information tasks and generalization from limited data.

Contribution

The paper presents the design, simulation, and analysis of QONNs, showcasing their ability to perform quantum tasks and generalize, advancing quantum neural network research.

Findings

QONNs can perform quantum state compression, reinforcement learning, and quantum simulation.

QONNs generalize well from small training datasets.

QONNs are promising for next-generation quantum processors.

Abstract

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.