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Connecting phases of matter to the flatness of the loss landscape in analog variational quantum algorithms

arXiv 2506.13865 ↗First posted 16 Jun 2025quant-ph cond-mat.dis-nn cs.LGcited by 0

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Abstract (arXiv)

Variational quantum algorithms (VQAs) promise near-term quantum advantage, yet parametrized quantum states commonly built from the digital gate-based approach often suffer from scalability issues such as barren plateaus, where the loss landscape becomes flat. We study an analog VQA ans"atze composed of M quenches of a disordered Ising chain, whose dynamics is native to several quantum simulation platforms. By tuning the disorder strength we place each quench in either a thermalized phase or a many-body-localized (MBL) phase and analyse (i) the ans"atze's expressivity and (ii) the scaling of loss variance. Numerics shows that both phases reach maximal expressivity at large M, but barren plateaus emerge at far smaller M in the thermalized phase than in the MBL phase. Exploiting this gap, we propose an MBL initialisation strategy: initialise the ans"atze in the MBL regime at intermediate quench M, enabling an initial trainability while retaining sufficient expressivity for subsequent optimization. The results link quantum phases of matter and VQA trainability, and provide practical guidelines for scaling analog-hardware VQAs.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 45 of its 150 references that are in the map. First posted on arXiv 16 Jun 2025.

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References · 45

  1. 2020Variational Quantum Algorithmscited by 692
  2. 2018Barren plateaus in quantum neural network training landscapescited by 630
  3. 2020The power of quantum neural networkscited by 379
  4. 2020Power of data in quantum machine learningcited by 338
  5. 2020The effect of data encoding on the expressive power of variational quantum machine learning modelscited by 333
  6. 2019Parameterized quantum circuits as machine learning modelscited by 332
  7. 2016Quantum Machine Learningcited by 326
  8. 2013Ising formulations of many NP problemscited by 207
  9. 2023Challenges and Opportunities in Quantum Machine Learningcited by 203
  10. 2020Noise-Induced Barren Plateaus in Variational Quantum Algorithmscited by 193
  11. 2021Connecting ansatz expressibility to gradient magnitudes and barren plateauscited by 180
  12. 2024Barren Plateaus in Variational Quantum Computingcited by 133
33 more
  1. 2020Absence of Barren Plateaus in Quantum Convolutional Neural Networkscited by 125
  2. 2020Entanglement Induced Barren Plateauscited by 101
  3. 2021Theory of overparametrization in quantum neural networkscited by 100
  4. 2021The Inductive Bias of Quantum Kernelscited by 91
  5. 2022Exponential concentration in quantum kernel methodscited by 90
  6. 2020Layerwise learning for quantum neural networkscited by 88
  7. 2020Effect of barren plateaus on gradient-free optimizationcited by 80
  8. 2023Does provable absence of barren plateaus imply classical simulability?cited by 80
  9. 2022Exploiting symmetry in variational quantum machine learningcited by 69
  10. 2019Learning to learn with quantum neural networks via classical neural networkscited by 56
  11. 2021Equivalence of quantum barren plateaus to cost concentration and narrow gorgescited by 53
  12. 2022Theoretical Guarantees for Permutation-Equivariant Quantum Neural Networkscited by 52
  13. 2022Escaping from the Barren Plateau via Gaussian Initializations in Deep Variational Quantum Circuitscited by 43
  14. 2023Challenges and Opportunities in Quantum Optimizationcited by 42
  15. 2022Classical surrogates for quantum learning modelscited by 39
  16. 2024Quantum Convolutional Neural Networks are Effectively Classically Simulablecited by 37
  17. 2020On barren plateaus and cost function locality in variational quantum algorithmscited by 36
  18. 2023Trainability barriers and opportunities in quantum generative modelingcited by 36
  19. 2021Subtleties in the trainability of quantum machine learning modelscited by 33
  20. 2021Capacity and quantum geometry of parametrized quantum circuitscited by 32
  21. 2023Potential and limitations of random Fourier features for dequantizing quantum machine learningcited by 22
  22. 2020Learning Unitaries by Gradient Descentcited by 20
  23. 2023On fundamental aspects of quantum extreme learning machinescited by 18
  24. 2023On the expressivity of embedding quantum kernelscited by 17
  25. 2024Variational quantum simulation: a case study for understanding warm startscited by 14
  26. 2023A Unified Framework for Trace-induced Quantum Kernelscited by 12
  27. 2024A Unified Theory of Quantum Neural Network Loss Landscapescited by 12
  28. 2022Hybrid Gate-Pulse Model for Variational Quantum Algorithmscited by 6
  29. 2025A unifying account of warm start guarantees for patches of quantum landscapescited by 5
  30. 2022NAPA: Intermediate-level Variational Native-pulse Ansatz for Variational Quantum Algorithmscited by 4
  31. 2024Efficient quantum-enhanced classical simulation for patches of quantum landscapescited by 4
  32. 2025Role of scrambling and noise in temporal information processing with quantum systemscited by 3
  33. 2021Hybrid Quantum-Classical Algorithms for Loan Collection Optimization with Loan Loss Provisionscited by 1

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