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.
Measurement-induced landscape transitions in hybrid variational quantum circuits
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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.