A hybrid variational quantum regression design with classical geometric preconditioning and curriculum optimization improves trainability over pure quantum models while remaining behind strong classical baselines.
Investigating and mitigating barren plateaus in variational quantum circuits: A survey
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AdaInit uses LLMs with submartingale properties to iteratively synthesize QNN initial parameters that maintain non-negligible gradient variance and mitigate barren plateaus, with claimed theoretical convergence guarantees and empirical outperformance.
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Geometric Preconditioning and Curriculum Optimization for Trainable Variational Quantum Regression
A hybrid variational quantum regression design with classical geometric preconditioning and curriculum optimization improves trainability over pure quantum models while remaining behind strong classical baselines.
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Large Language Models Can Help Mitigate Barren Plateaus in Quantum Neural Networks
AdaInit uses LLMs with submartingale properties to iteratively synthesize QNN initial parameters that maintain non-negligible gradient variance and mitigate barren plateaus, with claimed theoretical convergence guarantees and empirical outperformance.