论文标题
量子深度学习的进步：概述
Advances in Quantum Deep Learning: An Overview
论文作者
论文摘要
在过去的几十年中，深度学习和量子计算领域取得了重大突破。在这两个领域的交界处的研究引起了人们越来越多的兴趣，这导致了量子深度学习和量子启发的深度学习技术的发展。在这项工作中，我们通过讨论该领域的各种研究工作的技术贡献，优势和相似性，概述了量子计算和深度学习交集的进步。为此，我们审查并总结了为建模量子神经网络（QNN）和其他变体（例如量子卷积网络（QCNN））所提出的不同方案。我们还简要描述了量子启发的最新进展，经典的深度学习算法及其在自然语言处理中的应用。
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.
