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arXiv Machine Learning By Natsuto Isogai, Mio Murao, Hayata Yamasaki

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

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The paper introduces a classical algorithm that dequantizes a quantum sampler used for learning with optimized random features. By sampling heavy indices and reducing the transformation to a small principal block, the method produces a sparse classical representation with operator‑norm guarantees. This approach enables a classical sampler with specified accuracy and polynomial runtime, demonstrating that quantum block‑encoding factorizations can provide sufficient classical structure even when direct sampling access to the composite matrix is unavailable.

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