mlsanityData to 14 Oct 2026

A comprehensive review of Quantum Machine Learning: from NISQ to Fault Tolerance

arXiv 2401.11351 ↗First posted 21 Jan 2024quant-ph cs.AI cs.LGcited by 32

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Abstract (arXiv)

Quantum machine learning, which involves running machine learning algorithms on quantum devices, has garnered significant attention in both academic and business circles. In this paper, we offer a comprehensive and unbiased review of the various concepts that have emerged in the field of quantum machine learning. This includes techniques used in Noisy Intermediate-Scale Quantum (NISQ) technologies and approaches for algorithms compatible with fault-tolerant quantum computing hardware. Our review covers fundamental concepts, algorithms, and the statistical learning theory pertinent to quantum machine learning.

Key facts. Cited by 32 papers in the map (data through 14 Oct 2026). Builds on 45 of its 157 references that are in the map. First posted on arXiv 21 Jan 2024.

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45 of 157 references.

References · 45

  1. 2018On Lazy Training in Differentiable Programmingcited by 675
  2. 2018Supervised learning with quantum enhanced feature spacescited by 665
  3. 2018Barren plateaus in quantum neural network training landscapescited by 630
  4. 2020Power of data in quantum machine learningcited by 338
  5. 2020A rigorous and robust quantum speed-up in supervised machine learningcited by 271
  6. 2020Predicting Many Properties of a Quantum System from Very Few Measurementscited by 233
  7. 2020Noise-Induced Barren Plateaus in Variational Quantum Algorithmscited by 193
  8. 2021Quantum advantage in learning from experimentscited by 188
  9. 2021Connecting ansatz expressibility to gradient magnitudes and barren plateauscited by 180
  10. 2021The Principles of Deep Learning Theorycited by 173
  11. 2017Non-convex Optimization for Machine Learningcited by 133
  12. 2020Entanglement Induced Barren Plateauscited by 101
33 more
  1. 2021Information-theoretic bounds on quantum advantage in machine learningcited by 96
  2. 2017Quantum machine learning: a classical perspectivecited by 95
  3. 2021Quantum machine learning beyond kernel methodscited by 92
  4. 2020Robust data encodings for quantum classifierscited by 91
  5. 2022Exponential concentration in quantum kernel methodscited by 90
  6. 2020Effect of barren plateaus on gradient-free optimizationcited by 80
  7. 2023Does provable absence of barren plateaus imply classical simulability?cited by 80
  8. 2021Noisy intermediate-scale quantum (NISQ) algorithmscited by 79
  9. 2016Quantum Recommendation Systemscited by 70
  10. 2018A quantum-inspired classical algorithm for recommendation systemscited by 70
  11. 2019Stochastic gradient descent for hybrid quantum-classical optimizationcited by 59
  12. 2019Learning to learn with quantum neural networks via classical neural networkscited by 56
  13. 2016Efficient quantum tomography IIcited by 54
  14. 2021Equivalence of quantum barren plateaus to cost concentration and narrow gorgescited by 53
  15. 2021Towards understanding the power of quantum kernels in the NISQ eracited by 43
  16. 2021Representation Learning via Quantum Neural Tangent Kernelscited by 40
  17. 2019Sampling-based sublinear low-rank matrix arithmetic framework for dequantizing quantum machine learningcited by 37
  18. 2020On barren plateaus and cost function locality in variational quantum algorithmscited by 36
  19. 2018Quantum principal component analysis only achieves an exponential speedup because of its state preparation assumptionscited by 34
  20. 2017Information Perspective to Probabilistic Modeling: Boltzmann Machines versus Born Machinescited by 33
  21. 2019Low-depth gradient measurements can improve convergence in variational hybrid quantum-classical algorithmscited by 31
  22. 2023Improved machine learning algorithm for predicting ground state propertiescited by 22
  23. 2020Learning Unitaries by Gradient Descentcited by 20
  24. 2021Efficient estimation of Pauli observables by derandomizationcited by 20
  25. 2018JUNIPR: a Framework for Unsupervised Machine Learning in Particle Physicscited by 18
  26. 2020Computer-inspired Quantum Experimentscited by 18
  27. 2020Natural Evolutionary Strategies for Variational Quantum Computationcited by 15
  28. 2020Policy Gradient based Quantum Approximate Optimization Algorithmcited by 14
  29. 2021Can Error Mitigation Improve Trainability of Noisy Variational Quantum Algorithms?cited by 14
  30. 2021Revisiting dequantization and quantum advantage in learning taskscited by 12
  31. 2021Effects of quantum resources on the statistical complexity of quantum circuitscited by 9
  32. 2019Quantum Observables for continuous control of the Quantum Approximate Optimization Algorithm via Reinforcement Learningcited by 3
  33. 2021Classical algorithms and quantum limitations for maximum cut on high-girth graphscited by 3

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 6; 2025: 18; 2026: 8.

32 papers.

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  1. 2025A Primer on Quantum Machine Learningcited by 12
  2. 2025A PennyLane-Centric Dataset to Enhance LLM-based Quantum Code Generation using RAGcited by 9
  3. 2025Towards efficient quantum algorithms for diffusion probabilistic modelscited by 6
  4. 2025Quantum computing and artificial intelligence: status and perspectivescited by 6
  5. 2024Image Classification with Rotation-Invariant Variational Quantum Circuitscited by 3
  6. 2025Quantum Generative Adversarial Autoencoders: Learning latent representations for quantum data generationcited by 3
  7. 2024Regression and Classification with Single-Qubit Quantum Neural Networkscited by 2
  8. 2025TabularQGAN: A quantum generative model for tabular data synthesiscited by 2
  9. 2025SimQFL: A Quantum Federated Learning Simulator with Real-Time Visualizationcited by 2
  10. 2024Property-guided Inverse Design of Metal-Organic Frameworks Using Quantum Natural Language Processingcited by 1
  11. 2024Data-Dependent Generalization Bounds for Parameterized Quantum Models Under Noisecited by 1
  12. 2025HQCC: A Hybrid Quantum-Classical Classifier with Adaptive Structurecited by 1
20 more
  1. 2025Superior resilience to poisoning and amenability to unlearning in quantum machine learningcited by 1
  2. 2025Subsampling Factorization Machine Annealingcited by 1
  3. 2025Intrinsic preservation of plasticity in continual quantum learningcited by 1
  4. 2026The Role of Quantum in Hybrid Quantum-Classical Neural Networks: A Realistic Assessmentcited by 1
  5. 2026Quantum Circuit-Based Learning Models: Bridging Quantum Computing and Machine Learningcited by 1
  6. 2026Model selection in hybrid quantum neural networks with applications to quantum transformer architecturescited by 1
  7. 2026HQ-UNet: A Hybrid Quantum-Classical U-Net with a Quantum Bottleneck for Remote Sensing Image Segmentationcited by 1
  8. 2024Digital-Analog Quantum Machine Learningcited by 0
  9. 2024The Stabilizer Bootstrap of Quantum Machine Learning with up to 10000 qubitscited by 0
  10. 2025Single-Qudit Quantum Neural Networks for Multiclass Classificationcited by 0
  11. 2025Supervised Quantum Machine Learning: A Future Outlook from Qubits to Enterprise Applicationscited by 0
  12. 2025Noise tolerance via reinforcement: Learning a reinforced quantum dynamicscited by 0
  13. 2025On the Generalization Limits of Quantum Generative Adversarial Networks with Pure State Generatorscited by 0
  14. 2025QCSE: A Pretrained Quantum Context-Sensitive Word Embedding for Natural Language Processingcited by 0
  15. 2025Generative modeling using evolved quantum Boltzmann machinescited by 0
  16. 2025Hybrid Quantum-Classical Mixture of Experts: Unlocking Topological Advantage via Interference-Based Routingcited by 0
  17. 2026Quantum Scrambling Born Machinecited by 0
  18. 2026From Reachability to Learnability: Geometric Design Principles for Quantum Neural Networkscited by 0
  19. 2026Domain-Aware Hybrid Quantum Learning via Correlation-Guided Circuit Design for Crime Pattern Analyticscited by 0
  20. 2026Enhanced Reinforcement Learning-based Process Synthesis via Quantum Computingcited by 0