Author

Natsuto Isogai

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#machine learning Preprint Sep 2026

Advantage of Sample Complexity in Quantum PAC Learning Requires Inverse Access to State-Preparation Unitaries

Whether quantum computation can reduce the amount of data sampled from an unknown probability distribution required to learn a prediction rule is a fundamental question in quantum machine learning. Quantum PAC learning studies this question using quantum data as a quantum state whose squared amplitudes encode the unkno...

Natsuto Isogai, Satoshi Yoshida, M. Murao · 0 citations

#machine learning Preprint Sep 2026

A Quantum-Inspired Dequantization Method for Diagonally Weighted Matrix Functions: Application to Learning with Optimized Random Features

The results show that the factorization underlying a quantum block encoding can itself provide sufficient classical structure even when sampling-and-query access to the composite matrix is unavailable, suggesting a classical sampler with prescribed accuracy and polynomially related runtime.

Natsuto Isogai, M. Murao, Hayata Yamasaki · 0 citations