The quest for a Quantum Neural Network

TL;DR

This paper reviews the current state of Quantum Neural Networks, highlighting the challenges in integrating neural and quantum dynamics, and proposes future directions including open quantum neural networks with dissipative quantum computing.

Contribution

It provides a systematic review of existing QNN proposals, identifies gaps in leveraging quantum advantages, and suggests new research directions such as open quantum neural networks.

Findings

Existing QNN proposals do not fully exploit quantum advantages.

The integration of neural and quantum dynamics remains a significant challenge.

Open quantum neural networks based on dissipative quantum computing are a promising future direction.

Abstract

With the overwhelming success in the field of quantum information in the last decades, the "quest" for a Quantum Neural Network (QNN) model began in order to combine quantum computing with the striking properties of neural computing. This article presents a systematic approach to QNN research, which so far consists of a conglomeration of ideas and proposals. It outlines the challenge of combining the nonlinear, dissipative dynamics of neural computing and the linear, unitary dynamics of quantum computing. It establishes requirements for a meaningful QNN and reviews existing literature against these requirements. It is found that none of the proposals for a potential QNN model fully exploits both the advantages of quantum physics and computing in neural networks. An outlook on possible ways forward is given, emphasizing the idea of Open Quantum Neural Networks based on dissipative quantum computing.