  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.
