Quantum-Enhanced Bayesian Optimization for Algorithm Design

Jincheng Zhang

Published Sep 5, 2026• 0 citations

Abstract

Quantum-enhanced Bayesian Optimization (QEBO) represents a novel approach to algorithm design by extending the standard Bayesian Optimization framework to a quantum computing environment. This paper explores the potential of leveraging quantum annealing or quantum random sampling to accelerate the Bayesian Optimization process, thereby enabling the exploration of a significantly larger search space and the identification of optimal algorithm configurations. The core claim focuses on enhancing the search process through the discovery of subtle correlations facilitated by quantum computations. We detail the proposed mechanism, including the application of quantum algorithms to accelerate the Bayesian Optimization process, and analyze the potential benefits and challenges associated with this paradigm shift. The research investigates the efficacy of quantum-accelerated optimization in uncovering novel algorithmic patterns and optimizing existing algorithms.

DOI: 10.5281/zenodo.22321536

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