mlsanityData to 14 Oct 2026

Quantum computing and artificial intelligence: status and perspectives

arXiv 2505.23860 ↗First posted 29 May 2025quant-ph cs.AI cs.LGcited by 6

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Abstract (arXiv)

This white paper discusses and explores the various points of intersection between quantum computing and artificial intelligence (AI). It describes how quantum computing could support the development of innovative AI solutions. It also examines use cases of classical AI that can empower research and development in quantum technologies, with a focus on quantum computing and quantum sensing. The purpose of this white paper is to provide a long-term research agenda aimed at addressing foundational questions about how AI and quantum computing interact and benefit one another. It concludes with a set of recommendations and challenges, including how to orchestrate the proposed theoretical work, align quantum AI developments with quantum hardware roadmaps, estimate both classical and quantum resources - especially with the goal of mitigating and optimizing energy consumption - advance this emerging hybrid software engineering discipline, and enhance European industrial competitiveness while considering societal implications.

Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). Builds on 79 of its 215 references that are in the map. First posted on arXiv 29 May 2025.

Builds on

79 of 215 references.

References · 79

  1. 2020Variational Quantum Algorithmscited by 692
  2. 2018Supervised learning with quantum enhanced feature spacescited by 665
  3. 2018Barren plateaus in quantum neural network training landscapescited by 630
  4. 2020The power of quantum neural networkscited by 379
  5. 2020Power of data in quantum machine learningcited by 338
  6. 2020Predicting Many Properties of a Quantum System from Very Few Measurementscited by 233
  7. 2021Generalization in quantum machine learning from few training datacited by 216
  8. 2021Quantum advantage in learning from experimentscited by 188
  9. 2019Efficient Learning for Deep Quantum Neural Networkscited by 161
  10. 2019Flow-based generative models for Markov chain Monte Carlo in lattice field theorycited by 148
  11. 2020Explainable Reinforcement Learning: A Surveycited by 141
  12. 2024Barren Plateaus in Variational Quantum Computingcited by 133
38 more
  1. 2018Discovering physical concepts with neural networkscited by 117
  2. 2018Solving Statistical Mechanics Using Variational Autoregressive Networkscited by 100
  3. 2017Quantum machine learning: a classical perspectivecited by 95
  4. 2018Differentiable Learning of Quantum Circuit Born Machinecited by 93
  5. 2021Noisy intermediate-scale quantum (NISQ) algorithmscited by 79
  6. 2021Reinforcement learning for optimization of variational quantum circuit architecturescited by 77
  7. 2022On scientific understanding with artificial intelligencecited by 70
  8. 2020On the Quantum versus Classical Learnability of Discrete Distributionscited by 57
  9. 2018q-means: A quantum algorithm for unsupervised machine learningcited by 55
  10. 2022A Survey on Quantum Reinforcement Learningcited by 53
  11. 2023Understanding quantum machine learning also requires rethinking generalizationcited by 49
  12. 2023Learning to Decode the Surface Code with a Recurrent, Transformer-Based Neural Networkcited by 42
  13. 2023Challenges and Opportunities in Quantum Optimizationcited by 42
  14. 2017Active learning machine learns to create new quantum experimentscited by 41
  15. 2020Quantum State Tomography with Conditional Generative Adversarial Networkscited by 41
  16. 2022Classical surrogates for quantum learning modelscited by 39
  17. 2024Quantum Circuit Optimization with AlphaTensorcited by 36
  18. 2018Quantum principal component analysis only achieves an exponential speedup because of its state preparation assumptionscited by 34
  19. 2023Explainable Deep Reinforcement Learning: State of the Art and Challengescited by 33
  20. 2023Quantum circuit synthesis with diffusion modelscited by 33
  21. 2023Towards provably efficient quantum algorithms for large-scale machine-learning modelscited by 32
  22. 2023On quantum backpropagation, information reuse, and cheating measurement collapsecited by 32
  23. 2024A comprehensive review of Quantum Machine Learning: from NISQ to Fault Tolerancecited by 32
  24. 2023Shadows of quantum machine learningcited by 31
  25. 2018Optimizing Quantum Error Correction Codes with Reinforcement Learningcited by 29
  26. 2022Ab-initio quantum chemistry with neural-network wavefunctionscited by 29
  27. 2016Quantum Perceptron Modelscited by 28
  28. 2018Reinforcement Learning Decoders for Fault-Tolerant Quantum Computationcited by 28
  29. 2020Attention-based Quantum Tomographycited by 26
  30. 2023Exponential separations between classical and quantum learnerscited by 26
  31. 2024On the relation between trainability and dequantization of variational quantum learning modelscited by 25
  32. 2019The Mathematics of Text Structurecited by 24
  33. 2016Accelerate Monte Carlo Simulations with Restricted Boltzmann Machinescited by 21
  34. 2021Neural tensor contractions and the expressive power of deep neural quantum statescited by 21
  35. 2024Agents for self-driving laboratories applied to quantum computingcited by 19
  36. 2020Computer-inspired Quantum Experimentscited by 18
  37. 2017Learning hard quantum distributions with variational autoencoderscited by 17
  38. 2020Operationally meaningful representations of physical systems in neural networkscited by 16

50 most cited of 79 All 79 (JSON)

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 3; 2026: 3.

6 papers.

  1. 2025Artificial intelligence for representing and characterizing quantum systemscited by 10
  2. 2025TQml Simulator: optimized simulation of quantum machine learningcited by 3
  3. 2025Quantum Machine Learning in Multi-Qubit Phase-Space Part I: Foundationscited by 1
  4. 2026Automated near-term quantum algorithm discovery for molecular ground statescited by 1
  5. 2026Optimal algorithmic complexity of inference in quantum kernel methodscited by 1
  6. 2026When AI meets quantum information: A comprehensive reviewcited by 0