mlsanityData to 14 Oct 2026

A Quantum Generative Adversarial Network for distributions

arXiv 2110.02742 ↗First posted 4 Oct 2021quant-ph math.PR q-fin.CPcited by 7

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Abstract (arXiv)

Generative Adversarial Networks are becoming a fundamental tool in Machine Learning, in particular in the context of improving the stability of deep neural networks. At the same time, recent advances in Quantum Computing have shown that, despite the absence of a fault-tolerant quantum computer so far, quantum techniques are providing exponential advantage over their classical counterparts. We develop a fully connected Quantum Generative Adversarial network and show how it can be applied in Mathematical Finance, with a particular focus on volatility modelling.

Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). Builds on 20 of its 49 references that are in the map. First posted on arXiv 4 Oct 2021.

Builds on

20 of 49 references.

References · 20

  1. 2016Improved Techniques for Training GANscited by 2,970
  2. 2017Improved Training of Wasserstein GANscited by 2,235
  3. 2015Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networkscited by 2,054
  4. 2018Barren plateaus in quantum neural network training landscapescited by 630
  5. 2018Generative Image Inpainting with Contextual Attentioncited by 580
  6. 2015Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networkscited by 214
  7. 2018Quantum generative adversarial networkscited by 128
  8. 2018Deep Hedgingcited by 108
  9. 2018Generative Modeling using the Sliced Wasserstein Distancecited by 108
  10. 2019Quant GANs: Deep Generation of Financial Time Seriescited by 108
  11. 2020Experimental Quantum Generative Adversarial Networks for Image Generationcited by 83
  12. 2021Noisy intermediate-scale quantum (NISQ) algorithmscited by 79
8 more
  1. 2016Towards an integration of deep learning and neurosciencecited by 78
  2. 2020Generative Adversarial Networks (GANs Survey): Challenges, Solutions, and Future Directionscited by 65
  3. 2019Neural networks for option pricing and hedging: a literature reviewcited by 40
  4. 2019Quantum Wasserstein Generative Adversarial Networkscited by 33
  5. 2017Generative Adversarial Networks recover features in astrophysical images of galaxies beyond the deconvolution limitcited by 26
  6. 2019Generative Adversarial Networks for Financial Trading Strategies Fine-Tuning and Combinationcited by 22
  7. 2014On distinguishability criteria for estimating generative modelscited by 21
  8. 2019QGAN: Quantized Generative Adversarial Networkscited by 20

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 3; 2023: 1; 2024: 0; 2025: 1; 2026: 2.

7 papers.

  1. 2022Recent Advances for Quantum Neural Networks in Generative Learningcited by 39
  2. 2022Hybrid Quantum-Classical Generative Adversarial Network for High Resolution Image Generationcited by 22
  3. 2025A Primer on Quantum Machine Learningcited by 12
  4. 2022Quantum-probabilistic Hamiltonian learning for generative modelling & anomaly detectioncited by 7
  5. 2023Implementing Quantum Generative Adversarial Network (qGAN) and QCBM in Financecited by 1
  6. 2026Quantum Circuit-Based Learning Models: Bridging Quantum Computing and Machine Learningcited by 1
  7. 2026Quantum SEDONet: Spectrally-Embedded Quantum Deep Operator Networks for Partial Differential Equationscited by 0