mlsanityData to 7 Oct 2026

Advances in Quantum Deep Learning: An Overview

arXiv 2005.04316 ↗First posted 8 May 2020quant-ph cs.LGcited by 21

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Abstract (arXiv)

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.

Key facts. Cited by 21 papers in the map (data through 7 Oct 2026). First posted on arXiv 8 May 2020.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 3; 2022: 4; 2023: 7; 2024: 5; 2025: 1; 2026: 1.

21 papers.

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  1. 2021Quantum Neural Networks: Concepts, Applications, and Challengescited by 21
  2. 2022Rapid training of quantum recurrent neural networkscited by 13
  3. 2023Quantum Bayesian Optimizationcited by 12
  4. 2022A Hybrid Quantum-Classical Neural Network Architecture for Binary Classificationcited by 11
  5. 2023Quantum-Inspired Machine Learning: a Surveycited by 11
  6. 2022Toward Physically Realizable Quantum Neural Networkscited by 8
  7. 2021A Review of Machine Learning Classification Using Quantum Annealing for Real-world Applicationscited by 7
  8. 2022Evolution strategies: Application in hybrid quantum-classical neural networkscited by 5
  9. 2023Decentralized Quantum Federated Learning for Metaverse: Analysis, Design and Implementationcited by 5
  10. 2021Evaluating the Performance of Some Local Optimizers for Variational Quantum Classifierscited by 3
  11. 2023Projected Stochastic Gradient Descent with Quantum Annealed Binary Gradientscited by 3
  12. 2024Modeling stochastic eye tracking data: A comparison of quantum generative adversarial networks and Markov modelscited by 2
9 more
  1. 2023A Quantum Convolutional Neural Network Approach for Object Detection and Classificationcited by 1
  2. 2023KirchhoffNet: A Scalable Ultra Fast Analog Neural Networkcited by 1
  3. 2023Hybrid Quantum Neural Network in High-dimensional Data Classificationcited by 1
  4. 2024A Quantum-Classical Collaborative Training Architecture Based on Quantum State Fidelitycited by 1
  5. 2024Deep learning-based variational autoencoder for classification of quantum and classical states of lightcited by 1
  6. 2024Quantum Natural Stochastic Pairwise Coordinate Descentcited by 1
  7. 2024Distributed and Secure Kernel-Based Quantum Machine Learningcited by 1
  8. 2025Neural Architecture Search for Quantum Autoencoderscited by 1
  9. 2026Adaptive Quantum Physics-Informed Neural Networks for Differential Equations with Applications to Fluid Dynamicscited by 0