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The Adjoint Is All You Need: Characterizing Barren Plateaus in Quantum Ans\"atze

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arxiv 2309.07902 v4 pith:IFJHIBD5 submitted 2023-09-14 quant-ph

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keywords quantumansatztheoryalgebraatzebarrencostdynamical
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Using tools from the representation theory of compact Lie groups, we formulate a theory of Barren Plateaus (BPs) for parameterized quantum circuits whose observables lie in their dynamical Lie algebra (DLA), a setting that we term Lie algebra Supported Ansatz (LASA). A large variety of commonly used ans\"atze such as the Hamiltonian Variational Ansatz, Quantum Alternating Operator Ansatz, and many equivariant quantum neural networks are LASAs. In particular, our theory provides, for the first time, the ability to compute the variance of the gradient of the cost function of the quantum compound ansatz. We rigorously prove that, for LASA, the variance of the gradient of the cost function, for a 2-design of the dynamical Lie group, scales inversely with the dimension of the DLA, which agrees with existing numerical observations. In addition, to motivate the applicability of our results for 2-designs to practical settings, we show that rapid mixing occurs for LASAs with polynomial DLA. Lastly, we include potential extensions for handling cases when the observable lies outside of the DLA and the implications of our results.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Dynamical Lie Algebras Cannot Describe Shallow QAOA: Cragged Terrains, Barren Plateaus, and Empirical Hardness Models

    quant-ph 2026-08 conditional novelty 6.0 of 10

    For shallow QAOA on maximum independent set, loss landscape variance increases with system size instead of vanishing, contradicting dynamical Lie algebra predictions.

  2. Variational optical phase learning on a continuous-variable quantum compiler

    quant-ph 2025-02 conditional novelty 6.0 of 10

    An experimental continuous-variable quantum compiler learns an optical phase with two-mode squeezed light, and increasing the squeezing sharpens the cost landscape, improving precision and training speed.

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