Advances in Quantum Deep Learning: An Overview

TL;DR

This paper provides an overview of recent advances in quantum deep learning, highlighting the development of quantum neural networks, quantum-inspired algorithms, and their applications in natural language processing.

Contribution

It offers a comprehensive review of quantum deep learning techniques, models, and recent progress, summarizing key research developments in the field.

Findings

Quantum neural networks (QNNs) and quantum convolutional networks (QCNNs) have been proposed as models for quantum deep learning.

Quantum-inspired classical deep learning algorithms have shown progress and applications in natural language processing.

The paper identifies strengths and similarities among various quantum deep learning research works.

Abstract

The last few decades have seen significant breakthroughs in the fields of deep learning and quantum computing. Research at the junction of the two fields has garnered an increasing amount of interest, which has led to the development of quantum deep learning and quantum-inspired deep learning techniques in recent times. In this work, we present an overview of advances in the intersection of quantum computing and deep learning by discussing the technical contributions, strengths and similarities of various research works in this domain. To this end, we review and summarise the different schemes proposed to model quantum neural networks (QNNs) and other variants like quantum convolutional networks (QCNNs). We also briefly describe the recent progress in quantum inspired classic deep learning algorithms and their applications to natural language processing.