mlsanityData to 7 Oct 2026

Quantum Deep Learning

arXiv 1412.3489 ↗First posted 10 Dec 2014quant-ph cs.LG cs.NEcited by 31

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Abstract (arXiv)

In recent years, deep learning has had a profound impact on machine learning and artificial intelligence. At the same time, algorithms for quantum computers have been shown to efficiently solve some problems that are intractable on conventional, classical computers. We show that quantum computing not only reduces the time required to train a deep restricted Boltzmann machine, but also provides a richer and more comprehensive framework for deep learning than classical computing and leads to significant improvements in the optimization of the underlying objective function. Our quantum methods also permit efficient training of full Boltzmann machines and multi-layer, fully connected models and do not have well known classical counterparts.

Key facts. Cited by 31 papers in the map (data through 7 Oct 2026). First posted on arXiv 10 Dec 2014.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 8; 2022: 6; 2023: 10; 2024: 2; 2025: 4; 2026: 1.

31 papers.

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  1. 2023Challenges and Opportunities in Quantum Machine Learningcited by 202
  2. 2023Does provable absence of barren plateaus imply classical simulability?cited by 78
  3. 2023Quantum machine learning for image classificationcited by 56
  4. 2022Quantum Multi-Agent Reinforcement Learning via Variational Quantum Circuit Designcited by 25
  5. 2021Introduction to Quantum Reinforcement Learning: Theory and PennyLane-based Implementationcited by 23
  6. 2021Universal Adversarial Examples and Perturbations for Quantum Classifierscited by 22
  7. 2021Near-Optimal Quantum Algorithms for Multivariate Mean Estimationcited by 16
  8. 2022Quantum Distributed Deep Learning Architectures: Models, Discussions, and Applicationscited by 12
  9. 2022Laziness, Barren Plateau, and Noise in Machine Learningcited by 10
  10. 2023Splitting and Parallelizing of Quantum Convolutional Neural Networks for Learning Translationally Symmetric Datacited by 10
  11. 2021Quantum Mathematics in Artificial Intelligencecited by 5
  12. 2025Spectral Bias in Variational Quantum Machine Learningcited by 5
19 more
  1. 2021Evaluating the Performance of Some Local Optimizers for Variational Quantum Classifierscited by 3
  2. 2022Review of Ansatz Designing Techniques for Variational Quantum Algorithmscited by 3
  3. 2023Analogy between Boltzmann machines and Feynman path integralscited by 3
  4. 2023Projected Stochastic Gradient Descent with Quantum Annealed Binary Gradientscited by 3
  5. 2025Quantum advantage for learning shallow neural networks with natural data distributionscited by 3
  6. 2021Quantum Machine Learning For Classical Datacited by 2
  7. 2024Modeling stochastic eye tracking data: A comparison of quantum generative adversarial networks and Markov modelscited by 2
  8. 2021Quantum Speedup of Natural Gradient for Variational Bayescited by 1
  9. 2021Quantum Measurement Classification with Quditscited by 1
  10. 2022An efficient combination strategy for hybird quantum ensemble classifiercited by 1
  11. 2023KirchhoffNet: A Scalable Ultra Fast Analog Neural Networkcited by 1
  12. 2023Hybrid Quantum Neural Network in High-dimensional Data Classificationcited by 1
  13. 2025Instance-Optimal Matrix Multiplicative Weight Update and Its Quantum Applicationscited by 1
  14. 2025Accelerating Inference for Multilayer Neural Networks with Quantum Computerscited by 1
  15. 2022Estimating the randomness of quantum circuit ensembles up to 50 qubitscited by 0
  16. 2023Stochastic Security as a Performance Metric for Quantum-enhanced Generative AIcited by 0
  17. 2023Neural Crystalscited by 0
  18. 2024Distribution alignment based transfer fusion frameworks on quantum devices for seeking quantum advantagescited by 0
  19. 2026Breaking QAOA's Fixed Target Hamiltonian Barrier: A Fully Connected Quantum Boltzmann Machine via Bilevel Optimizationcited by 0