mlsanityData to 7 Oct 2026

Does provable absence of barren plateaus imply classical simulability?

arXiv 2312.09121 ↗First posted 14 Dec 2023quant-ph cs.LG stat.MLcited by 78

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Abstract (arXiv)

A large amount of effort has recently been put into understanding the barren plateau phenomenon. In this perspective article, we face the increasingly loud elephant in the room and ask a question that has been hinted at by many but not explicitly addressed: Can the structure that allows one to avoid barren plateaus also be leveraged to efficiently simulate the loss classically? We collect evidence-on a case-by-case basis-that many commonly used models whose loss landscapes avoid barren plateaus can also admit classical simulation, provided that one can collect some classical data from quantum devices during an initial data acquisition phase. This follows from the observation that barren plateaus result from a curse of dimensionality, and that current approaches for solving them end up encoding the problem into some small, classically simulable, subspaces. Thus, while stressing that quantum computers can be essential for collecting data, our analysis sheds doubt on the information processing capabilities of many parametrized quantum circuits with provably barren plateau-free landscapes. We end by discussing the (many) caveats in our arguments including the limitations of average case arguments, the role of smart initializations, models that fall outside our assumptions, the potential for provably superpolynomial advantages and the possibility that, once larger devices become available, parametrized quantum circuits could heuristically outperform our analytic expectations.

Key facts. Cited by 78 papers in the map (data through 7 Oct 2026). Builds on 57 of its 206 references that are in the map. First posted on arXiv 14 Dec 2023.

Builds on

57 of 206 references.

References · 57

  1. 2020Variational Quantum Algorithmscited by 687
  2. 2018Barren plateaus in quantum neural network training landscapescited by 624
  3. 2020The power of quantum neural networkscited by 377
  4. 2020Power of data in quantum machine learningcited by 336
  5. 2016Quantum Machine Learningcited by 325
  6. 2020Predicting Many Properties of a Quantum System from Very Few Measurementscited by 229
  7. 2021Generalization in quantum machine learning from few training datacited by 214
  8. 2023Challenges and Opportunities in Quantum Machine Learningcited by 202
  9. 2020Noise-Induced Barren Plateaus in Variational Quantum Algorithmscited by 192
  10. 2021Connecting ansatz expressibility to gradient magnitudes and barren plateauscited by 180
  11. 2020Absence of Barren Plateaus in Quantum Convolutional Neural Networkscited by 124
  12. 2020Entanglement Induced Barren Plateauscited by 101
38 more
  1. 2021Theory of overparametrization in quantum neural networkscited by 100
  2. 2021Information-theoretic bounds on quantum advantage in machine learningcited by 95
  3. 2022Exponential concentration in quantum kernel methodscited by 89
  4. 2019The Born Supremacy: Quantum Advantage and Training of an Ising Born Machinecited by 75
  5. 2022Exploiting symmetry in variational quantum machine learningcited by 68
  6. 2022Theory for Equivariant Quantum Neural Networkscited by 60
  7. 2020Trainability of Dissipative Perceptron-Based Quantum Neural Networkscited by 56
  8. 2022Group-Invariant Quantum Machine Learningcited by 56
  9. 2021Equivalence of quantum barren plateaus to cost concentration and narrow gorgescited by 53
  10. 2022Theoretical Guarantees for Permutation-Equivariant Quantum Neural Networkscited by 52
  11. 2023Understanding quantum machine learning also requires rethinking generalizationcited by 48
  12. 2022Escaping from the Barren Plateau via Gaussian Initializations in Deep Variational Quantum Circuitscited by 42
  13. 2022Equivariant quantum circuits for learning on weighted graphscited by 41
  14. 2022Representation Theory for Geometric Quantum Machine Learningcited by 40
  15. 2022Classical surrogates for quantum learning modelscited by 39
  16. 2020On barren plateaus and cost function locality in variational quantum algorithmscited by 36
  17. 2023Trainability barriers and opportunities in quantum generative modelingcited by 35
  18. 2022Learning to predict arbitrary quantum processescited by 34
  19. 2021Subtleties in the trainability of quantum machine learning modelscited by 33
  20. 2023On quantum backpropagation, information reuse, and cheating measurement collapsecited by 32
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  22. 2021Importance of Kernel Bandwidth in Quantum Machine Learningcited by 31
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  25. 2021Analyzing the barren plateau phenomenon in training quantum neural networks with the ZX-calculuscited by 30
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  27. 2021Exponentially Many Local Minima in Quantum Neural Networkscited by 22
  28. 2023Quantum Deep Hedgingcited by 22
  29. 2023Potential and limitations of random Fourier features for dequantizing quantum machine learningcited by 22
  30. 2023Exponential separations between classical and quantum learnerscited by 21
  31. 2023On the Sample Complexity of Quantum Boltzmann Machine Learningcited by 18
  32. 2018Adversarial quantum circuit learning for pure state approximationcited by 16
  33. 2021Optimal training of variational quantum algorithms without barren plateauscited by 15
  34. 2022BEINIT: Avoiding Barren Plateaus in Variational Quantum Algorithmscited by 15
  35. 2023The power and limitations of learning quantum dynamics incoherentlycited by 12
  36. 2023Quantum neural networks form Gaussian processescited by 11
  37. 2020Non-trivial symmetries in quantum landscapes and their resilience to quantum noisecited by 10
  38. 2022Inference-Based Quantum Sensingcited by 7

50 most cited of 57 All 57 (JSON)

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 1; 2024: 35; 2025: 26; 2026: 16.

78 papers.

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  1. 2024Barren Plateaus in Variational Quantum Computingcited by 130
  2. 2024Quantum Convolutional Neural Networks are Effectively Classically Simulablecited by 33
  3. 2024A comprehensive review of Quantum Machine Learning: from NISQ to Fault Tolerancecited by 32
  4. 2024On the relation between trainability and dequantization of variational quantum learning modelscited by 24
  5. 2023On fundamental aspects of quantum extreme learning machinescited by 18
  6. 2024Variational quantum simulation: a case study for understanding warm startscited by 14
  7. 2024VQC-Based Reinforcement Learning with Data Re-uploading: Performance and Trainabilitycited by 12
  8. 2024Training-efficient density quantum machine learningcited by 10
  9. 2024Investigating and Mitigating Barren Plateaus in Variational Quantum Circuits: A Surveycited by 9
  10. 2025Artificial intelligence for representing and characterizing quantum systemscited by 9
  11. 2025A Primer on Quantum Machine Learningcited by 9
  12. 2024Quantum Transformer: Accelerating model inference via quantum linear algebracited by 8
38 more
  1. 2024Trade-off between Gradient Measurement Efficiency and Expressivity in Deep Quantum Neural Networkscited by 7
  2. 2024Efficient Learning for Linear Properties of Bounded-Gate Quantum Circuitscited by 7
  3. 2024Adversarial Robustness Guarantees for Quantum Classifierscited by 6
  4. 2024In Search of Quantum Advantage: Estimating the Number of Shots in Quantum Kernel Methodscited by 6
  5. 2024Opportunities and limitations of explaining quantum machine learningcited by 6
  6. 2024Arbitrary Polynomial Separations in Trainable Quantum Machine Learningcited by 5
  7. 2024Quantum Neural Networks in Practice: A Comparative Study with Classical Models from Standard Data Sets to Industrial Imagescited by 5
  8. 2025Quantum-enhanced Computer Vision: Going Beyond Classical Algorithmscited by 5
  9. 2024Trainability issues in quantum policy gradientscited by 4
  10. 2024Resource-efficient equivariant quantum convolutional neural networkscited by 4
  11. 2024Entanglement-induced provable and robust quantum learning advantagescited by 4
  12. 2025Prospects for quantum advantage in machine learning from the representability of functionscited by 4
  13. 2026Gated QKAN-FWP: Scalable Quantum-inspired Sequence Learningcited by 4
  14. 2024Image Classification with Rotation-Invariant Variational Quantum Circuitscited by 3
  15. 2024Prospects of Privacy Advantage in Quantum Machine Learningcited by 3
  16. 2024Quantum Supervised Learningcited by 3
  17. 2024Concept learning of parameterized quantum models from limited measurementscited by 3
  18. 2025On the similarity of bandwidth-tuned quantum kernels and classical kernelscited by 3
  19. 2025Enhancing variational quantum algorithms by balancing training on classical and quantum hardwarecited by 3
  20. 2025Addressing the Current Challenges of Quantum Machine Learning through Multi-Chip Ensemblescited by 3
  21. 2025Role of scrambling and noise in temporal information processing with quantum systemscited by 3
  22. 2025TQml Simulator: optimized simulation of quantum machine learningcited by 3
  23. 2025Performance Guarantees for Quantum Neural Estimation of Entropiescited by 3
  24. 2026Spectral methods: crucial for machine learning, natural for quantum computers?cited by 3
  25. 2024Learning to Classify Quantum Phases of Matter with a Few Measurementscited by 2
  26. 2025Variational Quantum Optimization with Continuous Banditscited by 2
  27. 2025Adversarial Robustness of Partitioned Quantum Classifierscited by 2
  28. 2025TabularQGAN: A quantum generative model for tabular data synthesiscited by 2
  29. 2025Adversarial Robustness in Distributed Quantum Machine Learningcited by 2
  30. 2026How to find expressible and trainable parameterized quantum circuits?cited by 2
  31. 2026Non-variational supervised quantum kernel methods: a reviewcited by 2
  32. 2024Geometric quantum machine learning of BQPA protocols and latent graph classifierscited by 1
  33. 2024Architectures and random properties of symplectic quantum circuitscited by 1
  34. 2024QC-Forest: a Classical-Quantum Algorithm to Provably Speedup Retraining of Random Forestcited by 1
  35. 2024Quantum Curriculum Learningcited by 1
  36. 2024Derandomized shallow shadows: Efficient Pauli learning with bounded-depth circuitscited by 1
  37. 2025Inductive Graph Representation Learning with Quantum Graph Neural Networkscited by 1
  38. 2025Bayesian Quantum Orthogonal Neural Networks for Anomaly Detectioncited by 1

50 most cited of 78 All 78 (JSON)