Quantum-Inspired Optimization via Adaptive Ensemble Sampling

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

Quantum-inspired optimization aims to mimic the probabilistic nature of quantum mechanics to accelerate optimization algorithms. This paper proposes a novel approach utilizing adaptive ensemble sampling, a technique inspired by quantum mechanics, to enhance the exploration of the solution space. We demonstrate that this method significantly improves convergence speed and finds better solutions compared to traditional optimization techniques, particularly in complex, high-dimensional spaces. The core mechanism centers around dynamically adjusting the ensemble size based on the quality of candidate solutions, thereby promoting a more effective search process. The algorithm's effectiveness is quantified through extensive simulations and comparisons with established methods.

DOI: 10.5281/zenodo.22204499

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