Quantum-Enhanced Bayesian Optimization for Hyperparameter Tuning

Jincheng Zhang

Published Aug 29, 2026• 0 citations

Abstract

This paper investigates the potential of quantum computing to accelerate hyperparameter tuning within Bayesian optimization frameworks. Traditional Bayesian optimization can be computationally expensive, particularly when dealing with high-dimensional hyperparameter spaces. We propose a novel approach leveraging quantum amplitude estimation (QAE) to efficiently estimate the expected improvement function, a core component of Bayesian optimization. The theoretical framework outlines how QAE's speedup can translate into faster convergence and improved hyperparameter selection. We present a detailed mathematical description of the algorithm and its key components, emphasizing the transformation of the Bayesian optimization problem into a suitable form for quantum execution. The primary focus is on the theoretical advantages, without experimental validation, exploring the potential for significant speedups compared to classical Bayesian optimization methods. The paper concludes with a discussion of the challenges and future directions for this research area.

DOI: 10.5281/zenodo.22157427

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