A dynamical Lie algebra framework links circuit generator dimension to barren plateaus and shows that symmetry-preserving priors restrict algebra growth to polynomial scale, trading memorization capacity for gradient-rich landscapes.
A Geometric-Aware Perspec- tive and Beyond: Hybrid Quantum-Classical Machine Learning Methods,
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Beyond the Expressivity-Trainability Paradox: A Dynamical Lie Algebra Perspective on Navigating Barren Plateaus in Quantum Machine Learning
A dynamical Lie algebra framework links circuit generator dimension to barren plateaus and shows that symmetry-preserving priors restrict algebra growth to polynomial scale, trading memorization capacity for gradient-rich landscapes.