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Quantum Machine Learning

arXiv 1611.09347 ↗First posted 28 Nov 2016quant-ph cond-mat.str-el stat.MLcited by 325

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Abstract (arXiv)

Fuelled by increasing computer power and algorithmic advances, machine learning techniques have become powerful tools for finding patterns in data. Since quantum systems produce counter-intuitive patterns believed not to be efficiently produced by classical systems, it is reasonable to postulate that quantum computers may outperform classical computers on machine learning tasks. The field of quantum machine learning explores how to devise and implement concrete quantum software that offers such advantages. Recent work has made clear that the hardware and software challenges are still considerable but has also opened paths towards solutions.

Key facts. Cited by 325 papers in the map (data through 7 Oct 2026). First posted on arXiv 28 Nov 2016.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 43; 2022: 57; 2023: 66; 2024: 70; 2025: 74; 2026: 15.

325 papers.

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  1. 2021Generalization in quantum machine learning from few training datacited by 214
  2. 2023Challenges and Opportunities in Quantum Machine Learningcited by 202
  3. 2021Quantum advantage in learning from experimentscited by 186
  4. 2021Connecting ansatz expressibility to gradient magnitudes and barren plateauscited by 180
  5. 2024Barren Plateaus in Variational Quantum Computingcited by 130
  6. 2021Information-theoretic bounds on quantum advantage in machine learningcited by 95
  7. 2021Quantum machine learning beyond kernel methodscited by 91
  8. 2022Exponential concentration in quantum kernel methodscited by 89
  9. 2021Encoding-dependent generalization bounds for parametrized quantum circuitscited by 81
  10. 2021Noisy intermediate-scale quantum (NISQ) algorithmscited by 79
  11. 2023Does provable absence of barren plateaus imply classical simulability?cited by 78
  12. 2022Theory for Equivariant Quantum Neural Networkscited by 60
38 more
  1. 2022Group-Invariant Quantum Machine Learningcited by 56
  2. 2022Theoretical Guarantees for Permutation-Equivariant Quantum Neural Networkscited by 52
  3. 2022Out-of-distribution generalization for learning quantum dynamicscited by 49
  4. 2022Quantum Vision Transformerscited by 48
  5. 2023Understanding quantum machine learning also requires rethinking generalizationcited by 48
  6. 2021Quantum federated learning through blind quantum computingcited by 44
  7. 2021Towards understanding the power of quantum kernels in the NISQ eracited by 43
  8. 2021Enhancing Generative Models via Quantum Correlationscited by 40
  9. 2021Representation Learning via Quantum Neural Tangent Kernelscited by 40
  10. 2022Classical surrogates for quantum learning modelscited by 39
  11. 2022Experimental quantum adversarial learning with programmable superconducting qubitscited by 38
  12. 2023Quantum Data Encoding: A Comparative Analysis of Classical-to-Quantum Mapping Techniques and Their Impact on Machine Learning Accuracycited by 37
  13. 2023Trainability barriers and opportunities in quantum generative modelingcited by 35
  14. 2022Learning to predict arbitrary quantum processescited by 34
  15. 2021Subtleties in the trainability of quantum machine learning modelscited by 33
  16. 2022Systematic Literature Review: Quantum Machine Learning and its applicationscited by 32
  17. 2023Towards provably efficient quantum algorithms for large-scale machine-learning modelscited by 32
  18. 2023Shadows of quantum machine learningcited by 31
  19. 2023Generative quantum machine learning via denoising diffusion probabilistic modelscited by 30
  20. 2021Speeding up Learning Quantum States through Group Equivariant Convolutional Quantum Ans"atzecited by 29
  21. 2023Machine Learning for Anomaly Detection in Particle Physicscited by 29
  22. 2022Universal expressiveness of variational quantum classifiers and quantum kernels for support vector machinescited by 28
  23. 2022Benchmarking Adversarially Robust Quantum Machine Learning at Scalecited by 28
  24. 2021Automatic design of quantum feature mapscited by 27
  25. 2022Theoretical Error Performance Analysis for Variational Quantum Circuit Based Functional Regressioncited by 26
  26. 2023Towards quantum enhanced adversarial robustness in machine learningcited by 26
  27. 2021On the statistical complexity of quantum circuitscited by 25
  28. 2022Hybrid quantum neural network for drug response predictioncited by 25
  29. 2024Qiskit Code Assistant: Training LLMs for generating Quantum Computing Codecited by 24
  30. 2022The complexity of quantum support vector machinescited by 23
  31. 2024Quantum Kernel Methods under Scrutiny: A Benchmarking Studycited by 22
  32. 2022Analytic theory for the dynamics of wide quantum neural networkscited by 21
  33. 2023Quantum anomaly detection in the latent space of proton collision events at the LHCcited by 21
  34. 2023A Survey on Deep Learning Hardware Accelerators for Heterogeneous HPC Platformscited by 21
  35. 2023Quantum-Noise-Driven Generative Diffusion Modelscited by 20
  36. 2023Machine Learning for Practical Quantum Error Mitigationcited by 20
  37. 2021Style-based quantum generative adversarial networks for Monte Carlo eventscited by 19
  38. 2022Quantum Reinforcement Learning via Policy Iterationcited by 19

50 most cited of 325 All 325 (JSON)