Quantum-Inspired Bayesian Optimization for Complex Parameter Tuning

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

Quantum-Inspired Bayesian Optimization (QIBO) represents a paradigm shift in parameter tuning, moving beyond traditional optimization techniques to incorporate the principles of quantum mechanics. This paper investigates the application of QIBO to complex systems, specifically focusing on optimizing intricate parameter settings that exhibit inherent, non-linear relationships. We propose a novel algorithm that utilizes quantum-inspired concepts – specifically, a modified Monte Carlo integration – to enhance exploration and reduce variance, leading to more efficient and robust parameter estimations. The study will demonstrate the effectiveness of QIBO in tuning a challenging system involving a high-dimensional parameter space, highlighting its potential to surpass existing methods in terms of speed and accuracy.

DOI: 10.5281/zenodo.22203975

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