Adaptive Quantum Computing Probability Density Optimization

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

This paper investigates a novel approach to quantum state optimization utilizing a self-adaptive quantum computing algorithm. Traditional optimization methods often struggle with complex, high-dimensional parameter spaces. We propose a system that dynamically adjusts quantum state parameters based on probability density feedback, resulting in improved accuracy and efficiency. The core mechanism involves a closed-loop feedback system that continuously evaluates the probability density and adjusts the parameters to minimize the error. This adaptive strategy offers a significant improvement over static optimization techniques, particularly in scenarios involving noisy systems and complex quantum circuits. We present a theoretical analysis of the algorithm's performance and demonstrate its effectiveness through simulations.

DOI: 10.5281/zenodo.22200107

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