mlsanityData to 14 Oct 2026

Barren Plateaus in Variational Quantum Computing

arXiv 2405.00781 ↗First posted 1 May 2024quant-ph cs.LG stat.MLcited by 133

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Abstract (arXiv)

Variational quantum computing offers a flexible computational paradigm with applications in diverse areas. However, a key obstacle to realizing their potential is the Barren Plateau (BP) phenomenon. When a model exhibits a BP, its parameter optimization landscape becomes exponentially flat and featureless as the problem size increases. Importantly, all the moving pieces of an algorithm -- choices of ansatz, initial state, observable, loss function and hardware noise -- can lead to BPs when ill-suited. Due to the significant impact of BPs on trainability, researchers have dedicated considerable effort to develop theoretical and heuristic methods to understand and mitigate their effects. As a result, the study of BPs has become a thriving area of research, influencing and cross-fertilizing other fields such as quantum optimal control, tensor networks, and learning theory. This article provides a comprehensive review of the current understanding of the BP phenomenon.

Key facts. Cited by 133 papers in the map (data through 14 Oct 2026). Builds on 98 of its 321 references that are in the map. First posted on arXiv 1 May 2024.

Builds on

98 of 321 references.

References · 98

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38 more
  1. 2020A rigorous and robust quantum speed-up in supervised machine learningcited by 271
  2. 2023Challenges and Opportunities in Quantum Machine Learningcited by 203
  3. 2021Supervised quantum machine learning models are kernel methodscited by 200
  4. 2020Noise-Induced Barren Plateaus in Variational Quantum Algorithmscited by 193
  5. 2021Connecting ansatz expressibility to gradient magnitudes and barren plateauscited by 180
  6. 2018Dynamical Isometry and a Mean Field Theory of CNNs: How to Train 10,000-Layer Vanilla Convolutional Neural Networkscited by 176
  7. 2019Efficient Learning for Deep Quantum Neural Networkscited by 161
  8. 2020Absence of Barren Plateaus in Quantum Convolutional Neural Networkscited by 125
  9. 2019Quantum Natural Gradientcited by 106
  10. 2020Entanglement Induced Barren Plateauscited by 101
  11. 2021Theory of overparametrization in quantum neural networkscited by 100
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  13. 2021The Inductive Bias of Quantum Kernelscited by 91
  14. 2022Exponential concentration in quantum kernel methodscited by 90
  15. 2020Layerwise learning for quantum neural networkscited by 88
  16. 2020Effect of barren plateaus on gradient-free optimizationcited by 80
  17. 2023Does provable absence of barren plateaus imply classical simulability?cited by 80
  18. 2019The Born Supremacy: Quantum Advantage and Training of an Ising Born Machinecited by 76
  19. 2022Exploiting symmetry in variational quantum machine learningcited by 69
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  23. 2020Trainability of Dissipative Perceptron-Based Quantum Neural Networkscited by 56
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  25. 2021Equivalence of quantum barren plateaus to cost concentration and narrow gorgescited by 53
  26. 2022Theoretical Guarantees for Permutation-Equivariant Quantum Neural Networkscited by 52
  27. 2018Dynamical Isometry and a Mean Field Theory of RNNs: Gating Enables Signal Propagation in Recurrent Neural Networkscited by 45
  28. 2022Escaping from the Barren Plateau via Gaussian Initializations in Deep Variational Quantum Circuitscited by 43
  29. 2021Enhancing Generative Models via Quantum Correlationscited by 41
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  31. 2022Representation Theory for Geometric Quantum Machine Learningcited by 40
  32. 2020On barren plateaus and cost function locality in variational quantum algorithmscited by 36
  33. 2023Trainability barriers and opportunities in quantum generative modelingcited by 36
  34. 2021Subtleties in the trainability of quantum machine learning modelscited by 33
  35. 2021Capacity and quantum geometry of parametrized quantum circuitscited by 32
  36. 2022Bandwidth Enables Generalization in Quantum Kernel Modelscited by 32
  37. 2023A survey on the complexity of learning quantum statescited by 32
  38. 2021Importance of Kernel Bandwidth in Quantum Machine Learningcited by 31

50 most cited of 98 All 98 (JSON)

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 22; 2025: 54; 2026: 57.

133 papers.

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  1. 2024Quantum Convolutional Neural Networks are Effectively Classically Simulablecited by 37
  2. 2024On the relation between trainability and dequantization of variational quantum learning modelscited by 25
  3. 2025A Primer on Quantum Machine Learningcited by 12
  4. 2025Generative quantum advantage for classical and quantum problemscited by 11
  5. 2024Investigating and Mitigating Barren Plateaus in Variational Quantum Circuits: A Surveycited by 10
  6. 2025Benchmarking Quantum Reinforcement Learningcited by 10
  7. 2024Adversarial Robustness Guarantees for Quantum Classifierscited by 9
  8. 2024Reinforcement learning-based architecture search for quantum machine learningcited by 9
  9. 2025Double descent in quantum kernel methodscited by 9
  10. 2024Trade-off between Gradient Measurement Efficiency and Expressivity in Deep Quantum Neural Networkscited by 8
  11. 2025QKAN-LSTM: Quantum-inspired Kolmogorov-Arnold Long Short-term Memorycited by 8
  12. 2024Opportunities and limitations of explaining quantum machine learningcited by 7
38 more
  1. 2025TensorRL-QAS: Reinforcement learning with tensor networks for improved quantum architecture searchcited by 7
  2. 2024Mixed-State Quantum Denoising Diffusion Probabilistic Modelcited by 6
  3. 2025Quantum computing and artificial intelligence: status and perspectivescited by 6
  4. 2024D-Wave's Nonlinear-Program Hybrid Solver: Description and Performance Analysiscited by 5
  5. 2025Feedback-driven recurrent quantum neural network universalitycited by 5
  6. 2025Robust quantum reservoir computers for forecasting chaotic dynamics: generalized synchronization and stabilitycited by 5
  7. 2025TensorHyper-VQC: A Tensor-Train-Guided Hypernetwork for Robust and Scalable Variational Quantum Computingcited by 5
  8. 2024Resource-efficient equivariant quantum convolutional neural networkscited by 4
  9. 2025Prospects for quantum advantage in machine learning from the representability of functionscited by 4
  10. 2026Spectral methods: crucial for machine learning, natural for quantum computers?cited by 4
  11. 2026QNAS: A Neural Architecture Search Framework for Accurate and Efficient Quantum Neural Networkscited by 4
  12. 2026Gated QKAN-FWP: Scalable Quantum-inspired Sequence Learningcited by 4
  13. 2024Prospects of Privacy Advantage in Quantum Machine Learningcited by 3
  14. 2024Exact gradients for linear optics with single photonscited by 3
  15. 2025On the similarity of bandwidth-tuned quantum kernels and classical kernelscited by 3
  16. 2025Enhancing variational quantum algorithms by balancing training on classical and quantum hardwarecited by 3
  17. 2025Addressing the Current Challenges of Quantum Machine Learning through Multi-Chip Ensemblescited by 3
  18. 2025Role of scrambling and noise in temporal information processing with quantum systemscited by 3
  19. 2025It's-A-Me, Quantum Mario: Scalable Quantum Reinforcement Learning with Multi-Chip Ensemblescited by 3
  20. 2026Generative Quantum-inspired Kolmogorov-Arnold Eigensolvercited by 3
  21. 2024Security Concerns in Quantum Machine Learning as a Servicecited by 2
  22. 2024Quantum Kernel Learning for Small Dataset Modeling in Semiconductor Fabrication: Application to Ohmic Contactcited by 2
  23. 2024Expressivity of deterministic quantum computation with one qubitcited by 2
  24. 2025Variational Quantum Optimization with Continuous Banditscited by 2
  25. 2025Adversarial Robustness of Partitioned Quantum Classifierscited by 2
  26. 2025Quantum Doeblin Coefficients: Interpretations and Applicationscited by 2
  27. 2025A Numerical Gradient Inversion Attack in Variational Quantum Neural-Networkscited by 2
  28. 2025Practical insights on the effect of different encodings, ans"atze and measurements in quantum and hybrid convolutional neural networkscited by 2
  29. 2025BenchRL-QAS: Benchmarking reinforcement learning algorithms for quantum architecture searchcited by 2
  30. 2025Adversarial Robustness in Distributed Quantum Machine Learningcited by 2
  31. 2025Breaking Through Barren Plateaus: Reinforcement Learning Initializations for Deep Variational Quantum Circuitscited by 2
  32. 2025TITAN: A Trajectory-Informed Technique for Adaptive Parameter Freezing in Large-Scale VQEcited by 2
  33. 2025Learning Reduced Representations for Quantum Classifierscited by 2
  34. 2026Feature Entanglement-based Quantum Multimodal Fusion Neural Networkcited by 2
  35. 2026How to find expressible and trainable parameterized quantum circuits?cited by 2
  36. 2026Non-variational supervised quantum kernel methods: a reviewcited by 2
  37. 2024Architectures and random properties of symplectic quantum circuitscited by 1
  38. 2024QC-Forest: a Classical-Quantum Algorithm to Provably Speedup Retraining of Random Forestcited by 1

50 most cited of 133 All 133 (JSON)