<ul data-eligibleForWebStory="true"><li>GuiderNet is a meta-learning framework designed to optimize quantum circuit geometry and address issues with barren plateaus in Variational Quantum Algorithms.</li><li>It conditions Parameterized Quantum Circuits (PQCs) using data-dependent parameter shifts to minimize the log condition number of the Fubini-Study metric tensor.</li><li>GuiderNet has shown significant improvements in tasks like the Kaggle Diabetes classification by reducing training loss, increasing test accuracy, and improving generalization in quantum machine learning.</li><li>The framework suppresses gradient explosion, stabilizes parameter updates, and enhances trainability, demonstrating its potential to mitigate barren plateaus and ill-conditioning in quantum algorithms.</li></ul>

GuiderNet: A Meta-Learning Framework for Optimizing Quantum Circuit Geometry and Mitigating Barren Plateaus

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