mlsanityData to 7 Oct 2026

A Survey of Quantum Learning Theory

arXiv 1701.06806 ↗First posted 24 Jan 2017quant-ph cs.CC cs.LGcited by 11

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Abstract (arXiv)

This paper surveys quantum learning theory: the theoretical aspects of machine learning using quantum computers. We describe the main results known for three models of learning: exact learning from membership queries, and Probably Approximately Correct (PAC) and agnostic learning from classical or quantum examples.

Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). First posted on arXiv 24 Jan 2017.

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Citing papers per year: 2022: 2; 2023: 3; 2024: 5; 2025: 1.

11 papers.

  1. 2023On the average-case complexity of learning output distributions of quantum circuitscited by 9
  2. 2024Quantum Vision Transformers for Quark-Gluon Classificationcited by 7
  3. 2022Optimal quantum dataset for learning a unitary transformationcited by 6
  4. 2024Inference, interference and invariance: How the Quantum Fourier Transform can help to learn from datacited by 5
  5. 2023Generative Invertible Quantum Neural Networkscited by 3
  6. 2022Quantum Semi-Supervised Kernel Learningcited by 0
  7. 2023Fat Shattering, Joint Measurability, and PAC Learnability of POVM Hypothesis Classescited by 0
  8. 2024Quantum Reinforcement Learning in Non-Abelian Environments: Unveiling Novel Formulations and Quantum Advantage Explorationcited by 0
  9. 2024New Bounds on Quantum Sample Complexity of Measurement Classescited by 0
  10. 2024Lie-Equivariant Quantum Graph Neural Networkscited by 0
  11. 2025Hybrid Quantum-Classical Selective State Space Artificial Intelligencecited by 0