Quantum-Driven Constraint on Complex Systems

Jincheng Zhang

Published Sep 3, 2026• 0 citations

Abstract

Quantum-driven constraint on complex systems aims to accelerate solution processes by dynamically adjusting constraint parameters. This paper explores the potential of quantum annealing and variational quantum eigensolver (VQE) to optimize these parameters. The core claim is to implement an algorithm that dynamically adjusts constraint parameters on complex systems (e.g., protein folding, DNA sequencing) to achieve optimal solution speed and accuracy. The approach leverages quantum annealing for its ability to escape local optima and VQE for efficient exploration of the solution space. This research investigates the feasibility and potential advantages of employing quantum computing for optimization tasks within complex systems, offering a novel methodology for enhancing computational efficiency.

DOI: 10.5281/zenodo.22270992

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