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This research introduces a novel representation-theoretic framework to analyze barren plateaus in variational quantum algorithms, offering exact analytical bounds for cost-function variance applicable to arbitrary initial states and observables. By decomposing quantum landscapes into irreducible representation channels, the study extends previous Lie-algebraic results and identifies new trainability regimes for specific circuit architectures like the ANNNI model. These findings provide a more general theoretical tool for overcoming optimization barriers in scalable quantum computing.
A Representation-Theoretic Framework for Characterizing Barren Plateaus
arXiv:2609.04462v1 Announce Type: new Abstract: The scalability of variational quantum algorithms is fundamentally limited by the barren plateau effect, where the cost-function variance vanishes with system size, rendering optimization impractical. Recent Lie-algebraic approaches for deep parameterized have enabled a unified analytical understanding of this challenge but require either the initial state or the measurement observable to belong to the dynamical Lie algebra generated by the circuit. Here, we introduce a representation-theoretic framework under $2$-design hypothesis showing that variational quantum landscapes admit a natural decomposition into irreducible representation channels. This yields exact expressions and analytical bounds for the cost-function variance applicable to arbitrary initial states and observables, with previous Lie-algebraic results emerging as a special case. We illustrate the framework by analyzing the energy landscape of the one-dimensional ANNNI model for several circuit architectures, revealing trainability regimes inaccessible to existing methods. Our results establish a general representation-theoretic framework for analyzing variational quantum landscapes, substantially extending the analytical theory of barren plateaus.
Abstract:The scalability of variational quantum algorithms is fundamentally limited by the barren plateau effect, where the cost-function variance vanishes with system size, rendering optimization impractical. Recent Lie-algebraic approaches for deep parameterized have enabled a unified analytical understanding of this challenge but require either the initial state or the measurement observable to belong to the dynamical Lie algebra generated by the circuit. Here, we introduce a representation-theoretic framework under $2$-design hypothesis showing that variational quantum landscapes admit a natural decomposition into irreducible representation channels. This yields exact expressions and analytical bounds for the cost-function variance applicable to arbitrary initial states and observables, with previous Lie-algebraic results emerging as a special case. We illustrate the framework by analyzing the energy landscape of the one-dimensional ANNNI model for several circuit architectures, revealing trainability regimes inaccessible to existing methods. Our results establish a general representation-theoretic framework for analyzing variational quantum landscapes, substantially extending the analytical theory of barren plateaus.
arXiv:2609.04462 [quant-ph]
(or arXiv:2609.04462v1 [quant-ph] for this version)
https://doi.org/10.48550/arXiv.2609.04462 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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