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Title: Dynamic Constraint Optimization for Quantum Algorithms

Jincheng Zhang

Published Aug 31, 2026• 0 citations
Publisher

Abstract

Quantum algorithms, while promising, often suffer from computationally expensive optimization procedures. Traditional approaches are static, hindering adaptability and potentially leading to suboptimal results. This paper introduces a novel dynamic constraint optimization system leveraging reinforcement learning to dynamically adjust constraints during the execution of quantum algorithms. The system evolves constraints based on the algorithm's performance, aiming to improve efficiency and robustness. The core mechanism involves a reinforcement learning agent that learns to modify constraints, thereby enabling adaptive optimization within the quantum realm. This approach addresses the limitations of static optimization methods by providing a mechanism for dynamic adjustment, potentially unlocking new capabilities in quantum computation.

DOI
10.5281/zenodo.22193849

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