Interpretable machine learning is used to extract a universal shortest analytic quantum algorithm for arbitrary diagonal matrices of any size.
Structure Discovery in Nonparametric Regression through Compositional Kernel Search
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Bayesian optimization automates the scientific discovery cycle by modeling observations with surrogate models and using acquisition functions to select experiments that balance known information with new exploration.
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Machine Learning Approaches to Building Quantum Circuits for Sets of Matrices
Interpretable machine learning is used to extract a universal shortest analytic quantum algorithm for arbitrary diagonal matrices of any size.
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Efficient and Principled Scientific Discovery through Bayesian Optimization: A Tutorial
Bayesian optimization automates the scientific discovery cycle by modeling observations with surrogate models and using acquisition functions to select experiments that balance known information with new exploration.