mlsanityData to 7 Oct 2026

Decomposing Gradient Suppression in Barren Plateaus: Activity, Sign Organization, and Coupling

arXiv 2605.01319 ↗First posted 2 May 2026quant-ph cs.LGcited by 1

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Abstract (arXiv)

Barren plateaus (BPs) are conventionally characterized by suppressed gradient variance, but this aggregate description does not reveal how the loss of gradient signal is composed across Hamiltonian terms. We introduce a term-resolved framework that decomposes the second moment of the gradient exactly into pre-cancellation activity, sign organization, and their statistical coupling. A conditional random-sign model, which preserves termwise magnitudes while treating signs as independent and symmetric, provides exact references for organization and coupling. We apply the framework to a hardware-efficient ansatz (HEA) and a Hamiltonian variational ansatz (HVA) for the transverse-field and longitudinal-field Ising models. For the HEA, finite-size suppression of the second moment is carried almost entirely by decaying activity, accounting for 96.5-98.9% of the fitted log-slope across tested depths in both Hamiltonians, while organization shows no systematic scaling and coupling remains consistent with its random-sign reference. For the HVA, activity and organization instead grow with system size and contribute comparably to the second-moment scaling. A bias-corrected mean-gradient check is consistent with zero in every tested condition, so these results carry over approximately to the gradient variance. Microscopic sign-alignment analysis further shows sector-structured organization in the HVA, whereas the HEA exhibits only weak residual sign structure that does not accumulate into net signed organization. These patterns are reproduced across both Hamiltonians, providing a term-resolved characterization of BP-relevant gradient suppression beyond aggregate gradient statistics.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 7 of its 19 references that are in the map. First posted on arXiv 2 May 2026.

Builds on

7 of 19 references.

References · 7

  1. 2020Variational Quantum Algorithmscited by 687
  2. 2018Barren plateaus in quantum neural network training landscapescited by 624
  3. 2020Noise-Induced Barren Plateaus in Variational Quantum Algorithmscited by 192
  4. 2021Connecting ansatz expressibility to gradient magnitudes and barren plateauscited by 180
  5. 2024Barren Plateaus in Variational Quantum Computingcited by 130
  6. 2021Analyzing the barren plateau phenomenon in training quantum neural networks with the ZX-calculuscited by 30
  7. 2024Variational quantum simulation: a case study for understanding warm startscited by 14

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