mlsanityData to 14 Oct 2026

Variational Quantum Algorithms

arXiv 2012.09265 ↗First posted 16 Dec 2020quant-ph cs.LG stat.MLcited by 692

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Cited by 33 in Deep reinforcement learning methods, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

Applications such as simulating complicated quantum systems or solving large-scale linear algebra problems are very challenging for classical computers due to the extremely high computational cost. Quantum computers promise a solution, although fault-tolerant quantum computers will likely not be available in the near future. Current quantum devices have serious constraints, including limited numbers of qubits and noise processes that limit circuit depth. Variational Quantum Algorithms (VQAs), which use a classical optimizer to train a parametrized quantum circuit, have emerged as a leading strategy to address these constraints. VQAs have now been proposed for essentially all applications that researchers have envisioned for quantum computers, and they appear to the best hope for obtaining quantum advantage. Nevertheless, challenges remain including the trainability, accuracy, and efficiency of VQAs. Here we overview the field of VQAs, discuss strategies to overcome their challenges, and highlight the exciting prospects for using them to obtain quantum advantage.

Key facts. Cited by 692 papers in the map (data through 14 Oct 2026). First posted on arXiv 16 Dec 2020.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 65; 2022: 101; 2023: 94; 2024: 114; 2025: 158; 2026: 160.

692 papers.

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  1. 2021Generalization in quantum machine learning from few training datacited by 216
  2. 2023Challenges and Opportunities in Quantum Machine Learningcited by 203
  3. 2021Connecting ansatz expressibility to gradient magnitudes and barren plateauscited by 180
  4. 2024Barren Plateaus in Variational Quantum Computingcited by 133
  5. 2021Theory of overparametrization in quantum neural networkscited by 100
  6. 2021Quantum machine learning beyond kernel methodscited by 92
  7. 2021The Inductive Bias of Quantum Kernelscited by 91
  8. 2022Exponential concentration in quantum kernel methodscited by 90
  9. 2021Federated Quantum Machine Learningcited by 82
  10. 2021Encoding-dependent generalization bounds for parametrized quantum circuitscited by 82
  11. 2023Does provable absence of barren plateaus imply classical simulability?cited by 80
  12. 2021Reinforcement learning for optimization of variational quantum circuit architecturescited by 77
38 more
  1. 2021Training Quantum Embedding Kernels on Near-Term Quantum Computerscited by 69
  2. 2022Exploiting symmetry in variational quantum machine learningcited by 69
  3. 2022Theory for Equivariant Quantum Neural Networkscited by 62
  4. 2021The Dawn of Quantum Natural Language Processingcited by 61
  5. 2022Group-Invariant Quantum Machine Learningcited by 56
  6. 2021Equivalence of quantum barren plateaus to cost concentration and narrow gorgescited by 53
  7. 2022Theoretical Guarantees for Permutation-Equivariant Quantum Neural Networkscited by 52
  8. 2021Variational Quantum Reinforcement Learning via Evolutionary Optimizationcited by 50
  9. 2023Understanding quantum machine learning also requires rethinking generalizationcited by 49
  10. 2024Training of Physical Neural Networkscited by 49
  11. 2022Quantum Vision Transformerscited by 48
  12. 2021Quantum federated learning through blind quantum computingcited by 44
  13. 2021Towards understanding the power of quantum kernels in the NISQ eracited by 43
  14. 2022Escaping from the Barren Plateau via Gaussian Initializations in Deep Variational Quantum Circuitscited by 43
  15. 2022Quantum Self-Attention Neural Networks for Text Classificationcited by 43
  16. 2022Equivariant quantum circuits for learning on weighted graphscited by 41
  17. 2021Representation Learning via Quantum Neural Tangent Kernelscited by 40
  18. 2022Representation Theory for Geometric Quantum Machine Learningcited by 40
  19. 2022Recent Advances for Quantum Neural Networks in Generative Learningcited by 39
  20. 2024Curriculum reinforcement learning for quantum architecture search under hardware errorscited by 37
  21. 2024KANQAS: Kolmogorov-Arnold Network for Quantum Architecture Searchcited by 37
  22. 2024Quantum Convolutional Neural Networks are Effectively Classically Simulablecited by 37
  23. 2021Recent advances for quantum classifierscited by 35
  24. 2022Learning to predict arbitrary quantum processescited by 35
  25. 2021Subtleties in the trainability of quantum machine learning modelscited by 33
  26. 2023Towards provably efficient quantum algorithms for large-scale machine-learning modelscited by 32
  27. 2023On quantum backpropagation, information reuse, and cheating measurement collapsecited by 32
  28. 2024Quantum Architecture Search: A Surveycited by 32
  29. 2021Quantum machine learning with differential privacycited by 31
  30. 2023Shadows of quantum machine learningcited by 31
  31. 2023Generative quantum machine learning via denoising diffusion probabilistic modelscited by 30
  32. 2021Speeding up Learning Quantum States through Group Equivariant Convolutional Quantum Ans"atzecited by 29
  33. 2023A Survey on Quantum Machine Learning: Current Trends, Challenges, Opportunities, and the Road Aheadcited by 29
  34. 2021Entangled Datasets for Quantum Machine Learningcited by 27
  35. 2022projUNN: efficient method for training deep networks with unitary matricescited by 27
  36. 2022Theoretical Error Performance Analysis for Variational Quantum Circuit Based Functional Regressioncited by 26
  37. 2022PQLM -- Multilingual Decentralized Portable Quantum Language Model for Privacy Protectioncited by 26
  38. 2021On the statistical complexity of quantum circuitscited by 25

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