mlsanityData to 7 Oct 2026

TensorFlow Quantum: A Software Framework for Quantum Machine Learning

arXiv 2003.02989 ↗First posted 6 Mar 2020quant-ph cond-mat.dis-nn cs.LGcited by 72

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Abstract (arXiv)

We introduce TensorFlow Quantum (TFQ), an open source library for the rapid prototyping of hybrid quantum-classical models for classical or quantum data. This framework offers high-level abstractions for the design and training of both discriminative and generative quantum models under TensorFlow and supports high-performance quantum circuit simulators. We provide an overview of the software architecture and building blocks through several examples and review the theory of hybrid quantum-classical neural networks. We illustrate TFQ functionalities via several basic applications including supervised learning for quantum classification, quantum control, simulating noisy quantum circuits, and quantum approximate optimization. Moreover, we demonstrate how one can apply TFQ to tackle advanced quantum learning tasks including meta-learning, layerwise learning, Hamiltonian learning, sampling thermal states, variational quantum eigensolvers, classification of quantum phase transitions, generative adversarial networks, and reinforcement learning. We hope this framework provides the necessary tools for the quantum computing and machine learning research communities to explore models of both natural and artificial quantum systems, and ultimately discover new quantum algorithms which could potentially yield a quantum advantage.

Key facts. Cited by 72 papers in the map (data through 7 Oct 2026). First posted on arXiv 6 Mar 2020.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 17; 2022: 12; 2023: 13; 2024: 10; 2025: 12; 2026: 8.

72 papers.

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  1. 2023Challenges and Opportunities in Quantum Machine Learningcited by 202
  2. 2021Quantum advantage in learning from experimentscited by 186
  3. 2021Connecting ansatz expressibility to gradient magnitudes and barren plateauscited by 180
  4. 2021Quantum machine learning beyond kernel methodscited by 91
  5. 2021Parametrized quantum policies for reinforcement learningcited by 87
  6. 2021Equivalence of quantum barren plateaus to cost concentration and narrow gorgescited by 53
  7. 2021Quantum Federated Learning with Quantum Datacited by 53
  8. 2022Dissipative Hamiltonian Neural Networks: Learning Dissipative and Conservative Dynamics Separatelycited by 46
  9. 2022Recent Advances for Quantum Neural Networks in Generative Learningcited by 39
  10. 2021Recent advances for quantum classifierscited by 35
  11. 2022Systematic Literature Review: Quantum Machine Learning and its applicationscited by 32
  12. 2023A Survey on Quantum Machine Learning: Current Trends, Challenges, Opportunities, and the Road Aheadcited by 29
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  1. 2022Hybrid quantum neural network for drug response predictioncited by 25
  2. 2023A Survey on Deep Learning Hardware Accelerators for Heterogeneous HPC Platformscited by 21
  3. 2021Quantum Self-Supervised Learningcited by 20
  4. 2021Hybrid Quantum Classical Graph Neural Networks for Particle Track Reconstructioncited by 20
  5. 2022Multiclass classification using quantum convolutional neural networks with hybrid quantum-classical learningcited by 16
  6. 2021A semi-agnostic ansatz with variable structure for quantum machine learningcited by 15
  7. 2022Quantum Deep Reinforcement Learning for Robot Navigation Taskscited by 14
  8. 2021Graph neural network initialisation of quantum approximate optimisationcited by 13
  9. 2022Quantum Neural Network Classifiers: A Tutorialcited by 13
  10. 2023Quantum-Inspired Machine Learning: a Surveycited by 11
  11. 2024Training-efficient density quantum machine learningcited by 10
  12. 2025QCPINN: Quantum-Classical Physics-Informed Neural Networks for Solving PDEscited by 9
  13. 2025Generative quantum advantage for classical and quantum problemscited by 9
  14. 2021Optimizing Quantum Variational Circuits with Deep Reinforcement Learningcited by 8
  15. 2022Towards Quantum Graph Neural Networks: An Ego-Graph Learning Approachcited by 8
  16. 2022Quantum neural network autoencoder and classifier applied to an industrial case studycited by 8
  17. 2024Qiskit-Torch-Module: Fast Prototyping of Quantum Neural Networkscited by 8
  18. 2022Scalable Quantum Neural Networks for Classificationcited by 6
  19. 2021Comparing concepts of quantum and classical neural network models for image classification taskcited by 5
  20. 2021Hybrid Quantum-Classical Neural Network for Cloud-supported In-Vehicle Cyberattack Detectioncited by 4
  21. 2023Quantum Convolutional Neural Networks for Multi-Channel Supervised Learningcited by 4
  22. 2022Provably efficient variational generative modeling of quantum many-body systems via quantum-probabilistic information geometrycited by 3
  23. 2023Projected Stochastic Gradient Descent with Quantum Annealed Binary Gradientscited by 3
  24. 2023sQUlearn -- A Python Library for Quantum Machine Learningcited by 3
  25. 2024Do Quantum Neural Networks have Simplicity Bias?cited by 3
  26. 2024Exploiting the equivalence between quantum neural networks and perceptronscited by 3
  27. 2021Entanglement Diagnostics for Efficient Quantum Computationcited by 2
  28. 2023VQNet 2.0: A New Generation Machine Learning Framework that Unifies Classical and Quantumcited by 2
  29. 2023Can I say, now machines can think?cited by 2
  30. 2023Several fitness functions and entanglement gates in quantum kernel generationcited by 2
  31. 2024QuanTest: Entanglement-Guided Testing of Quantum Neural Network Systemscited by 2
  32. 2025Predictive control of blast furnace temperature in steelmaking with hybrid depth-infused quantum neural networkscited by 2
  33. 2025Towards Adapting Federated & Quantum Machine Learning for Network Intrusion Detection: A Surveycited by 2
  34. 2021Gaussian boson sampling and multi-particle event optimization by machine learning in the quantum phase spacecited by 1
  35. 2023Quantum Imitation Learningcited by 1
  36. 2023Quantum Shadow Gradient Descent for Variational Quantum Algorithmscited by 1
  37. 2024Hype or Heuristic? Quantum Reinforcement Learning for Join Order Optimisationcited by 1
  38. 2024Dynamic Spectrum Access for Ambient Backscatter Communication-assisted D2D Systems with Quantum Reinforcement Learningcited by 1

50 most cited of 72 All 72 (JSON)