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Advances in Quantum Deep Learning: An Overview

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arxiv 2005.04316 v1 pith:7KZAEALO submitted 2020-05-08 quant-ph cs.LG

Siddhant Garg , Goutham Ramakrishnan This is my paper · ORCID
classification quant-phcs.LG
keywords quantumdeeplearningadvancescomputingfieldsnetworksoverview
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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.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Quantum Data Sketches

    cs.DB 2025-01 reject novelty 6.0 of 10

    The authors design dimension-independent classical sketches that approximately preserve the trace distance between quantum states, supporting approximate database operations without full state tomography.

  2. Adaptive Quantum Physics-Informed Neural Networks for Differential Equations with Applications to Fluid Dynamics

    cs.LG 2026-08 conditional novelty 4.0 of 10

    Adaptive collocation sampling and learned loss weights improve accuracy of quantum physics-informed neural networks on six differential-equation benchmarks, including Burgers and Navier-Stokes flows.

  3. Quantum Deep Learning for Massive MIMO User Scheduling

    eess.SP 2025-08 conditional novelty 4.0 of 10

    A hybrid quantum-classical neural network trained by reinforcement learning is claimed to beat a CNN for massive MIMO user scheduling in simulated Rician channels.

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