Adaptive Probability Field: A Quantum Computing Optimization Algorithm

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

This paper introduces an adaptive probability field optimization algorithm based on quantum computing. Traditional optimization algorithms often rely on fixed parameters, which can limit their effectiveness in complex optimization problems. This work proposes a novel approach utilizing quantum mechanics to dynamically adjust probability field parameters, enabling more efficient and accurate solutions. The core mechanism involves introducing a quantumized probability field and leveraging quantum phenomena like superposition and entanglement to represent and manipulate the parameters in a way that adapts to the specific characteristics of the problem. The algorithm's potential lies in its ability to overcome the limitations of traditional optimization techniques by offering a more flexible and powerful approach to problem-solving. We explore the theoretical foundations and preliminary experimental results demonstrating the effectiveness of this approach, highlighting its advantages and potential applications.

DOI: 10.5281/zenodo.22183712

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