mlsanityData to 7 Oct 2026

Challenges and Opportunities in Quantum Machine Learning

arXiv 2303.09491 ↗First posted 16 Mar 2023quant-ph cs.LG stat.MLcited by 202

Not on the map. Why?

Abstract (arXiv)

At the intersection of machine learning and quantum computing, Quantum Machine Learning (QML) has the potential of accelerating data analysis, especially for quantum data, with applications for quantum materials, biochemistry, and high-energy physics. Nevertheless, challenges remain regarding the trainability of QML models. Here we review current methods and applications for QML. We highlight differences between quantum and classical machine learning, with a focus on quantum neural networks and quantum deep learning. Finally, we discuss opportunities for quantum advantage with QML.

Key facts. Cited by 202 papers in the map (data through 7 Oct 2026). Builds on 54 of its 135 references that are in the map. First posted on arXiv 16 Mar 2023.

Builds on

54 of 135 references.

References · 54

  1. 2021Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gaugescited by 1,131
  2. 2020Variational Quantum Algorithmscited by 687
  3. 2018Supervised learning with quantum enhanced feature spacescited by 654
  4. 2018Barren plateaus in quantum neural network training landscapescited by 624
  5. 2020The power of quantum neural networkscited by 377
  6. 2020Power of data in quantum machine learningcited by 336
  7. 2020The effect of data encoding on the expressive power of variational quantum machine learning modelscited by 328
  8. 2016Quantum Machine Learningcited by 325
  9. 2020A rigorous and robust quantum speed-up in supervised machine learningcited by 270
  10. 2021Generalization in quantum machine learning from few training datacited by 214
  11. 2021Supervised quantum machine learning models are kernel methodscited by 199
  12. 2020Noise-Induced Barren Plateaus in Variational Quantum Algorithmscited by 192
38 more
  1. 2021Quantum advantage in learning from experimentscited by 186
  2. 2021Connecting ansatz expressibility to gradient magnitudes and barren plateauscited by 180
  3. 2019Efficient Learning for Deep Quantum Neural Networkscited by 160
  4. 2018Quantum generative adversarial networkscited by 128
  5. 2020Absence of Barren Plateaus in Quantum Convolutional Neural Networkscited by 124
  6. 2019Quantum Natural Gradientcited by 104
  7. 2020Entanglement Induced Barren Plateauscited by 101
  8. 2021Theory of overparametrization in quantum neural networkscited by 100
  9. 2021Information-theoretic bounds on quantum advantage in machine learningcited by 95
  10. 2020Robust data encodings for quantum classifierscited by 91
  11. 2021Provably efficient machine learning for quantum many-body problemscited by 91
  12. 2021The Inductive Bias of Quantum Kernelscited by 90
  13. 2021Encoding-dependent generalization bounds for parametrized quantum circuitscited by 81
  14. 2020Recurrent Quantum Neural Networkscited by 79
  15. 2020Effect of barren plateaus on gradient-free optimizationcited by 79
  16. 2018Continuous-variable quantum neural networkscited by 74
  17. 2020TensorFlow Quantum: A Software Framework for Quantum Machine Learningcited by 72
  18. 2018A quantum-inspired classical algorithm for recommendation systemscited by 70
  19. 2021Training Quantum Embedding Kernels on Near-Term Quantum Computerscited by 69
  20. 2022Exploiting symmetry in variational quantum machine learningcited by 68
  21. 2019Quantum Graph Neural Networkscited by 66
  22. 2019Stochastic gradient descent for hybrid quantum-classical optimizationcited by 59
  23. 2020Trainability of Dissipative Perceptron-Based Quantum Neural Networkscited by 56
  24. 2022Group-Invariant Quantum Machine Learningcited by 56
  25. 2018q-means: A quantum algorithm for unsupervised machine learningcited by 55
  26. 2019Learning to learn with quantum neural networks via classical neural networkscited by 55
  27. 2021Equivalence of quantum barren plateaus to cost concentration and narrow gorgescited by 53
  28. 2021Exponential separations between learning with and without quantum memorycited by 50
  29. 2022Out-of-distribution generalization for learning quantum dynamicscited by 49
  30. 2022Equivariant quantum circuits for learning on weighted graphscited by 41
  31. 2019Sampling-based sublinear low-rank matrix arithmetic framework for dequantizing quantum machine learningcited by 37
  32. 2020On barren plateaus and cost function locality in variational quantum algorithmscited by 36
  33. 2021Subtleties in the trainability of quantum machine learning modelscited by 33
  34. 2014Quantum Deep Learningcited by 31
  35. 2020Reformulation of the No-Free-Lunch Theorem for Entangled Data Setscited by 28
  36. 2019Quantum Hamiltonian-Based Models and the Variational Quantum Thermalizer Algorithmcited by 27
  37. 2021Entangled Datasets for Quantum Machine Learningcited by 26
  38. 2020Learning Unitaries by Gradient Descentcited by 20

50 most cited of 54 All 54 (JSON)

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 47; 2024: 47; 2025: 71; 2026: 37.

202 papers.

Sort:

By area:

  1. 2024Barren Plateaus in Variational Quantum Computingcited by 130
  2. 2023Does provable absence of barren plateaus imply classical simulability?cited by 78
  3. 2024Training of Physical Neural Networkscited by 47
  4. 2023Quantum circuit synthesis with diffusion modelscited by 33
  5. 2024Quantum Convolutional Neural Networks are Effectively Classically Simulablecited by 33
  6. 2023Machine Learning for Anomaly Detection in Particle Physicscited by 29
  7. 2023QKSAN: A Quantum Kernel Self-Attention Networkcited by 23
  8. 2024Quantum Kernel Methods under Scrutiny: A Benchmarking Studycited by 22
  9. 2023Training robust and generalizable quantum modelscited by 21
  10. 2023Towards Quantum Federated Learningcited by 20
  11. 2024Learning shallow quantum circuitscited by 20
  12. 2023On the Sample Complexity of Quantum Boltzmann Machine Learningcited by 18
38 more
  1. 2023Quantum Long Short-Term Memory (QLSTM) vs Classical LSTM in Time Series Forecasting: A Comparative Study in Solar Power Forecastingcited by 15
  2. 2023Hybrid quantum image classification and federated learning for hepatic steatosis diagnosiscited by 15
  3. 2023Quantum Machine Learning on Near-Term Quantum Devices: Current State of Supervised and Unsupervised Techniques for Real-World Applicationscited by 14
  4. 2023On the Interpretability of Quantum Neural Networkscited by 13
  5. 2023Reduction of finite sampling noise in quantum neural networkscited by 12
  6. 2023A Unified Framework for Trace-induced Quantum Kernelscited by 12
  7. 2023Quantum neural networks form Gaussian processescited by 11
  8. 2024Quantum Mixed-State Self-Attention Networkcited by 11
  9. 2023Splitting and Parallelizing of Quantum Convolutional Neural Networks for Learning Translationally Symmetric Datacited by 10
  10. 2024Training-efficient density quantum machine learningcited by 10
  11. 2023Quantum Gaussian Process Regression for Bayesian Optimizationcited by 9
  12. 2024Quantum Generative Diffusion Model: A Fully Quantum-Mechanical Model for Generating Quantum State Ensemblecited by 9
  13. 2024Equilibrium Propagation: the Quantum and the Thermal Casescited by 9
  14. 2025A Primer on Quantum Machine Learningcited by 9
  15. 2023Quantum-limited stochastic optical neural networks operating at a few quanta per activationcited by 8
  16. 2023Symmetry-invariant quantum machine learning force fieldscited by 8
  17. 2024Reinforcement learning-based architecture search for quantum machine learningcited by 8
  18. 2025QKAN-LSTM: Quantum-inspired Kolmogorov-Arnold Long Short-term Memorycited by 8
  19. 2023A supervised hybrid quantum machine learning solution to the emergency escape routing problemcited by 7
  20. 2023All you need is spin: SU(2) equivariant variational quantum circuits based on spin networkscited by 7
  21. 2024Quantum delegated and federated learning via quantum homomorphic encryptioncited by 7
  22. 2025Quantum-Enhanced Parameter-Efficient Learning for Typhoon Trajectory Forecastingcited by 7
  23. 2025Quantum-Informed Machine Learning for Predicting Spatiotemporal Chaos with Practical Quantum Advantagecited by 7
  24. 2024Efficient Parameter Optimisation for Quantum Kernel Alignment: A Sub-sampling Approach in Variational Trainingcited by 6
  25. 2023QuMoS: A Framework for Preserving Security of Quantum Machine Learning Modelcited by 5
  26. 2023On the Role of Entanglement and Statistics in Learningcited by 5
  27. 2023Transition Role of Entangled Data in Quantum Machine Learningcited by 5
  28. 2023Hierarchical Learning for Quantum ML: Novel Training Technique for Large-Scale Variational Quantum Circuitscited by 5
  29. 2023Fast Quantum Convolutional Neural Networks for Low-Complexity Object Detection in Autonomous Driving Applicationscited by 5
  30. 2024GQHAN: A Grover-inspired Quantum Hard Attention Networkcited by 5
  31. 2024Constructing Optimal Noise Channels for Enhanced Robustness in Quantum Machine Learningcited by 5
  32. 2025Experimentally validated quantum-secure federated learning over a multi-user quantum networkcited by 5
  33. 2025Data re-uploading in Quantum Machine Learning for time series: application to traffic forecastingcited by 5
  34. 2025ATP: Adaptive Threshold Pruning for Efficient Data Encoding in Quantum Neural Networkscited by 5
  35. 2025TensorHyper-VQC: A Tensor-Train-Guided Hypernetwork for Robust and Scalable Variational Quantum Computingcited by 5
  36. 2025Quantum-enhanced Computer Vision: Going Beyond Classical Algorithmscited by 5
  37. 2023Variational Denoising for Variational Quantum Eigensolvercited by 4
  38. 2023Quantum Convolutional Neural Networks for Multi-Channel Supervised Learningcited by 4

50 most cited of 202 All 202 (JSON)