Title: Quantum-Induced Constraint Dynamics (QCD)

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

Quantum-Induced Constraint Dynamics (QCD) explores the potential of utilizing quantum entanglement to dynamically adjust constraint parameters within optimization algorithms, offering a novel approach to accelerating solution convergence. This research investigates how entanglement within the optimization process can act as a feedback loop, rapidly converging to optimal solutions. We propose a mechanism whereby a 'quantum constraint' parameter alters the entanglement, triggering a rapid feedback cycle that optimizes the solution. This work presents a theoretical framework and preliminary simulations demonstrating the feasibility of this approach. The study aims to uncover a fundamental shift in optimization, leveraging quantum mechanics to achieve unprecedented speed and efficiency.

DOI: 10.5281/zenodo.22197717

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