Quantum-Enhanced Bayesian Optimization for Complex Model Selection

Jincheng Zhang

Published Sep 1, 2026• 0 citations

Abstract

Quantum-enhanced Bayesian optimization (QEBO) offers a promising approach to accelerate the model selection process, particularly for complex models, by harnessing quantum mechanics for enhanced efficiency in parameter space exploration. This paper proposes a novel QEBO algorithm utilizing quantum state estimation to rapidly approximate the fitness of model configurations, thereby reducing computational cost and improving the selection of optimal models. We introduce a quantum-enhanced Bayesian optimization framework, focusing on the exploitation of quantum entanglement and superposition to efficiently map the parameter space. The core mechanism involves quantum state estimation to quickly assess the suitability of each model configuration, significantly reducing the search time compared to classical methods. This paper details the theoretical foundation, algorithm implementation, and preliminary experimental results demonstrating the potential of QEBO to outperform traditional Bayesian optimization in complex model selection scenarios.

DOI: 10.5281/zenodo.22219159

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