Does provable absence of barren plateaus imply classical simulability?
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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
- 2020Variational Quantum Algorithmscited by 687
- 2018Barren plateaus in quantum neural network training landscapescited by 624
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- 2016Quantum Machine Learningcited by 325
- 2020Predicting Many Properties of a Quantum System from Very Few Measurementscited by 229
- 2021Generalization in quantum machine learning from few training datacited by 214
- 2023Challenges and Opportunities in Quantum Machine Learningcited by 202
- 2020Noise-Induced Barren Plateaus in Variational Quantum Algorithmscited by 192
- 2021Connecting ansatz expressibility to gradient magnitudes and barren plateauscited by 180
- 2020Absence of Barren Plateaus in Quantum Convolutional Neural Networkscited by 124
- 2020Entanglement Induced Barren Plateauscited by 101
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- 2022Exploiting symmetry in variational quantum machine learningcited by 68
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- 2020Trainability of Dissipative Perceptron-Based Quantum Neural Networkscited by 56
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- 2021Equivalence of quantum barren plateaus to cost concentration and narrow gorgescited by 53
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- 2020On barren plateaus and cost function locality in variational quantum algorithmscited by 36
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- 2021Subtleties in the trainability of quantum machine learning modelscited by 33
- 2023On quantum backpropagation, information reuse, and cheating measurement collapsecited by 32
- 2014Quantum Deep Learningcited by 31
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- 2021Analyzing the barren plateau phenomenon in training quantum neural networks with the ZX-calculuscited by 30
- 2021Speeding up Learning Quantum States through Group Equivariant Convolutional Quantum Ans"atzecited by 29
- 2021Exponentially Many Local Minima in Quantum Neural Networkscited by 22
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- 2023On the Sample Complexity of Quantum Boltzmann Machine Learningcited by 18
- 2018Adversarial quantum circuit learning for pure state approximationcited by 16
- 2021Optimal training of variational quantum algorithms without barren plateauscited by 15
- 2022BEINIT: Avoiding Barren Plateaus in Variational Quantum Algorithmscited by 15
- 2023The power and limitations of learning quantum dynamics incoherentlycited by 12
- 2023Quantum neural networks form Gaussian processescited by 11
- 2020Non-trivial symmetries in quantum landscapes and their resilience to quantum noisecited by 10
- 2022Inference-Based Quantum Sensingcited by 7
50 most cited of 57 All 57 (JSON)
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Citing papers per year: 2023: 1; 2024: 35; 2025: 26; 2026: 16.
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- 2024Quantum Convolutional Neural Networks are Effectively Classically Simulablecited by 33
- 2024A comprehensive review of Quantum Machine Learning: from NISQ to Fault Tolerancecited by 32
- 2024On the relation between trainability and dequantization of variational quantum learning modelscited by 24
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- 2024Variational quantum simulation: a case study for understanding warm startscited by 14
- 2024VQC-Based Reinforcement Learning with Data Re-uploading: Performance and Trainabilitycited by 12
- 2024Training-efficient density quantum machine learningcited by 10
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- 2026QuPID: Quantum Parameter-Efficient Input-Dependent Retrieval Adaptation for Medical RAG27 Sep 2026
- 2026Stacking the Deck: Tunable Trainability in Stacked LCUs27 Jul 2026
- 2026Entanglement geometry separates circuit cutting, classical hardness, and trainability20 Jul 2026
- 2026Scalable Message-Passing Quantum Graph Neural Networks in the Weisfeiler-Leman Hierarchy25 Jun 2026
- 2026Higher-Order Token Interactions via Quantum Attention10 Jun 2026
- 2026Gated QKAN-FWP: Scalable Quantum-inspired Sequence Learning7 May 2026
- 2026Magic-Informed Quantum Architecture Search5 May 2026
- 2026Trainability Beyond Linearity in Variational Quantum Objectives20 Apr 2026
- 2026Reachability Constraints in Variational Quantum Circuits: Optimization within Polynomial Group Module15 Apr 2026
- 2026Non-variational supervised quantum kernel methods: a review9 Apr 2026
- 2026Spectral methods: crucial for machine learning, natural for quantum computers?25 Mar 2026
- 2026How to find expressible and trainable parameterized quantum circuits?15 Mar 2026
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