Neural networks with quantum states of light

TL;DR

This paper reviews the current state and potential of quantum optical neural networks, emphasizing how quantum light states and squeezing can enhance machine learning and information processing capabilities.

Contribution

It provides a perspective on quantum optical machine learning, discussing novel applications like quantum reservoir computing and associative memories with quantum advantages.

Findings

Quantum optical networks enable advanced machine learning applications.

Light squeezing enhances the capabilities of quantum neural network architectures.

Quantum substrates offer potential advantages in complex, recurrent, and coherent interactions.

Abstract

Quantum optical networks are instrumental to address fundamental questions and enable applications ranging from communication to computation and, more recently, machine learning. In particular, photonic artificial neural networks offer the opportunity to exploit the advantages of both classical and quantum optics. Photonic neuro-inspired computation and machine learning have been successfully demonstrated in classical settings, while quantum optical networks have triggered breakthrough applications such as teleportation, quantum key distribution and quantum computing. We present a perspective on the state of the art in quantum optical machine learning and the potential advantages of artificial neural networks in circuit designs and beyond, in more general analogue settings characterised by recurrent and coherent complex interactions. We consider two analogue neuro-inspired applications, namely quantum reservoir computing and quantum associative memories, and discuss the enhanced capabilities offered by quantum substrates, highlighting the specific role of light squeezing in this context.