mlsanityData to 7 Oct 2026

Recent Advances for Quantum Neural Networks in Generative Learning

arXiv 2206.03066 ↗First posted 7 Jun 2022quant-ph cs.CV cs.LGcited by 39

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Abstract (arXiv)

Quantum computers are next-generation devices that hold promise to perform calculations beyond the reach of classical computers. A leading method towards achieving this goal is through quantum machine learning, especially quantum generative learning. Due to the intrinsic probabilistic nature of quantum mechanics, it is reasonable to postulate that quantum generative learning models (QGLMs) may surpass their classical counterparts. As such, QGLMs are receiving growing attention from the quantum physics and computer science communities, where various QGLMs that can be efficiently implemented on near-term quantum machines with potential computational advantages are proposed. In this paper, we review the current progress of QGLMs from the perspective of machine learning. Particularly, we interpret these QGLMs, covering quantum circuit born machines, quantum generative adversarial networks, quantum Boltzmann machines, and quantum autoencoders, as the quantum extension of classical generative learning models. In this context, we explore their intrinsic relation and their fundamental differences. We further summarize the potential applications of QGLMs in both conventional machine learning tasks and quantum physics. Last, we discuss the challenges and further research directions for QGLMs.

Key facts. Cited by 39 papers in the map (data through 7 Oct 2026). Builds on 102 of its 323 references that are in the map. First posted on arXiv 7 Jun 2022.

Builds on

102 of 323 references.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 3; 2023: 9; 2024: 11; 2025: 10; 2026: 6.

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  1. 2023SpectralGPT: Spectral Remote Sensing Foundation Modelcited by 250
  2. 2024Training of Physical Neural Networkscited by 47
  3. 2022Problem-Dependent Power of Quantum Neural Networks on Multi-Class Classificationcited by 20
  4. 2023Multimodal deep representation learning for quantum cross-platform verificationcited by 9
  5. 2024Quantum Generative Diffusion Model: A Fully Quantum-Mechanical Model for Generating Quantum State Ensemblecited by 9
  6. 2025Artificial intelligence for representing and characterizing quantum systemscited by 9
  7. 2023ShadowNet for Data-Centric Quantum System Learningcited by 7
  8. 2024Neural auto-designer for enhanced quantum kernelscited by 7
  9. 2024Efficient Learning for Linear Properties of Bounded-Gate Quantum Circuitscited by 7
  10. 2024From Graphs to Qubits: A Critical Review of Quantum Graph Neural Networkscited by 6
  11. 2023Transition Role of Entangled Data in Quantum Machine Learningcited by 5
  12. 20233D-QAE: Fully Quantum Auto-Encoding of 3D Point Cloudscited by 5
27 more
  1. 2025ATP: Adaptive Threshold Pruning for Efficient Data Encoding in Quantum Neural Networkscited by 5
  2. 2022Implementation of Trained Factorization Machine Recommendation System on Quantum Annealercited by 4
  3. 2024Quantum Compiling with Reinforcement Learning on a Superconducting Processorcited by 4
  4. 2025Quantum Generative Models for Image Generation: Insights from MNIST and MedMNISTcited by 4
  5. 2025Rethink the Role of Deep Learning towards Large-scale Quantum Systemscited by 4
  6. 2023Generative Invertible Quantum Neural Networkscited by 3
  7. 2025Quantum Generative Adversarial Autoencoders: Learning latent representations for quantum data generationcited by 3
  8. 2024VAE-QWGAN: Addressing Mode Collapse in Quantum GANs via Autoencoding Priorscited by 2
  9. 2025Overcoming Dimensional Factorization Limits in Discrete Diffusion Models through Quantum Joint Distribution Learningcited by 2
  10. 2022Shuffle-QUDIO: accelerate distributed VQE with trainability enhancement and measurement reductioncited by 1
  11. 2023Quantum Autoencoders for Learning Quantum Channel Codescited by 1
  12. 2023Statistical Analysis of Quantum State Learning Process in Quantum Neural Networkscited by 1
  13. 2024Separable Power of Classical and Quantum Learning Protocols Through the Lens of No-Free-Lunch Theoremcited by 1
  14. 2024SuperEncoder: Towards Universal Neural Approximate Quantum State Preparationcited by 1
  15. 2025A Distributed Hybrid Quantum Convolutional Neural Network for Medical Image Classificationcited by 1
  16. 2025A Comprehensively Adaptive Architectural Optimization-Ingrained Quantum Neural Network Model for Cloud Workloads Predictioncited by 1
  17. 2025PALQO: Physics-informed Model for Accelerating Large-scale Quantum Optimizationcited by 1
  18. 2026Model selection in hybrid quantum neural networks with applications to quantum transformer architecturescited by 1
  19. 2026Quantum-classical physics-informed Kolmogorov-Arnold networks for PDEscited by 1
  20. 2023Optical Quantum Sensing for Agnostic Environments via Deep Learningcited by 0
  21. 2024Evaluation of QCNN-LSTM for Disability Forecasting in Multiple Sclerosis Using Sequential Multisequence MRIcited by 0
  22. 2024OrganiQ: Mitigating Classical Resource Bottlenecks of Quantum Generative Adversarial Networks on NISQ-Era Machinescited by 0
  23. 2025Quantum-Enhanced Generative Models for Rare Event Predictioncited by 0
  24. 2026Differentiable Architecture Search for Adversarially Robust Quantum Computer Visioncited by 0
  25. 2026Q-Tag: Watermarking Quantum Circuit Generative Modelscited by 0
  26. 2026On the Approximation Complexity of Matrix Product Operator Born Machinescited by 0
  27. 2026Quantum-Assisted Memory-Efficient Training for Parameter-Intensive Wi-Fi-Based Human Activity Recognitioncited by 0