mlsanityData to 7 Oct 2026

A Primer on Quantum Machine Learning

arXiv 2511.15969 ↗First posted 20 Nov 2025quant-ph cs.AI cs.LGcited by 9

Not on the map. Why?

Abstract (arXiv)

Quantum machine learning (QML) is a computational paradigm that seeks to apply quantum-mechanical resources to solve learning problems. As such, the goal of this framework is to leverage quantum processors to tackle optimization, supervised, unsupervised and reinforcement learning, and generative modeling-among other tasks-more efficiently than classical models. Here we offer a high level overview of QML, focusing on settings where the quantum device is the primary learning or data generating unit. We outline the field's tensions between practicality and guarantees, access models and speedups, and classical baselines and claimed quantum advantages-flagging where evidence is strong, where it is conditional or still lacking, and where open questions remain. By shedding light on these nuances and debates, we aim to provide a friendly map of the QML landscape so that the reader can judge when-and under what assumptions-quantum approaches may offer real benefits.

Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). Builds on 168 of its 516 references that are in the map. First posted on arXiv 20 Nov 2025.

Builds on

168 of 516 references.

References · 168

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2020Language Models are Few-Shot Learnerscited by 32,199
  3. 2019An Introduction to Variational Autoencoderscited by 997
  4. 2020Deep Reinforcement Learning for Autonomous Driving: A Surveycited by 698
  5. 2020Variational Quantum Algorithmscited by 687
  6. 2018Supervised learning with quantum enhanced feature spacescited by 654
  7. 2018Barren plateaus in quantum neural network training landscapescited by 624
  8. 2020The power of quantum neural networkscited by 377
  9. 2020Power of data in quantum machine learningcited by 336
  10. 2020A Survey on Neural Network Interpretabilitycited by 291
  11. 2020A rigorous and robust quantum speed-up in supervised machine learningcited by 270
  12. 2020Predicting Many Properties of a Quantum System from Very Few Measurementscited by 229
38 more
  1. 2021Generalization in quantum machine learning from few training datacited by 214
  2. 2023Challenges and Opportunities in Quantum Machine Learningcited by 202
  3. 2021Supervised quantum machine learning models are kernel methodscited by 199
  4. 2020Noise-Induced Barren Plateaus in Variational Quantum Algorithmscited by 192
  5. 2021Quantum advantage in learning from experimentscited by 186
  6. 2019Variational Quantum Circuits for Deep Reinforcement Learningcited by 165
  7. 2017Deep Reinforcement Learning for De-Novo Drug Designcited by 161
  8. 2024Barren Plateaus in Variational Quantum Computingcited by 130
  9. 2020Absence of Barren Plateaus in Quantum Convolutional Neural Networkscited by 124
  10. 2021Recent Advances in Reinforcement Learning in Financecited by 102
  11. 2021Theory of overparametrization in quantum neural networkscited by 100
  12. 2021Information-theoretic bounds on quantum advantage in machine learningcited by 95
  13. 2020Robust data encodings for quantum classifierscited by 91
  14. 2021Quantum machine learning beyond kernel methodscited by 91
  15. 2021The Inductive Bias of Quantum Kernelscited by 90
  16. 2021Parametrized quantum policies for reinforcement learningcited by 87
  17. 2024Better than classical? The subtle art of benchmarking quantum machine learning modelscited by 82
  18. 2016Quantum-enhanced machine learningcited by 79
  19. 2020Recurrent Quantum Neural Networkscited by 79
  20. 2023Does provable absence of barren plateaus imply classical simulability?cited by 78
  21. 2019The Born Supremacy: Quantum Advantage and Training of an Ising Born Machinecited by 75
  22. 2020Reinforcement Learning with Quantum Variational Circuitscited by 74
  23. 2018A quantum-inspired classical algorithm for recommendation systemscited by 70
  24. 2016Quantum Recommendation Systemscited by 69
  25. 2021Training Quantum Embedding Kernels on Near-Term Quantum Computerscited by 69
  26. 2022Exploiting symmetry in variational quantum machine learningcited by 68
  27. 2019Quantum Graph Neural Networkscited by 66
  28. 2022Theory for Equivariant Quantum Neural Networkscited by 60
  29. 2020On the Quantum versus Classical Learnability of Discrete Distributionscited by 57
  30. 2022Group-Invariant Quantum Machine Learningcited by 56
  31. 2018q-means: A quantum algorithm for unsupervised machine learningcited by 55
  32. 2022Theoretical Guarantees for Permutation-Equivariant Quantum Neural Networkscited by 52
  33. 2021Variational Quantum Reinforcement Learning via Evolutionary Optimizationcited by 50
  34. 2021Exponential separations between learning with and without quantum memorycited by 50
  35. 2022A Survey on Quantum Reinforcement Learningcited by 50
  36. 2022Out-of-distribution generalization for learning quantum dynamicscited by 49
  37. 2018Universal Quantum Control through Deep Reinforcement Learningcited by 44
  38. 2021Towards understanding the power of quantum kernels in the NISQ eracited by 43

50 most cited of 168 All 168 (JSON)

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 2; 2026: 7.

9 papers.

  1. 2025Adaptive Conformal Prediction for Quantum Machine Learningcited by 0
  2. 2025Generative modeling using evolved quantum Boltzmann machinescited by 0
  3. 2026Learning Quantum Data Distribution via Chaotic Quantum Diffusion Modelcited by 0
  4. 2026QPredSGG: Hybrid Quantum Predicate Learning for Long-Tailed Scene Graph Generationcited by 0
  5. 2026Ravines in quantum cost landscapes: opportunities for improved VQA predictionscited by 0
  6. 2026Canonical quantization of neuronscited by 0
  7. 2026Quantum Topological Data Encodingcited by 0
  8. 2026Volcanic Clouds Detection through QCNN and Geostationary Satellite Multispectral Imagerycited by 0
  9. 2026Quantum Gaussian processes for prediction of channel observationscited by 0