Title: Adaptive Geometry-Based Constraint Propagation for Quantum Algorithms

Jincheng Zhang

Published Sep 2, 2026• 0 citations

Abstract

Quantum algorithms are notoriously sensitive to initial conditions, making their design and optimization a complex endeavor. Traditional constraint propagation methods often rely on static or simplified models, failing to fully exploit the intricate geometry of the solution space. This paper introduces an Adaptive Geometry-Based Constraint Propagation framework designed to dynamically adjust the algorithm based on the geometry of the solution, resulting in improved efficiency and accuracy. We propose a novel approach that continuously refines the constraint propagation strategy, leveraging geometric insights to optimize the quantum algorithm's performance. This work addresses the inherent limitations of static constraint propagation by introducing a mechanism to adaptively adjust the geometry of the solution space. The core of this framework involves a dynamic geometric analysis, where the algorithm iteratively updates its parameters based on the resulting geometry, ultimately leading to enhanced quantum algorithm performance.

DOI: 10.5281/zenodo.22253734

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