Self-Adaptive Quantum Algorithm Optimization

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

This paper presents a novel approach to quantum algorithm optimization utilizing self-adaptive geometric constraints. Traditional optimization methods often rely on classical parameters, while this work introduces a quantum-based strategy that leverages the inherent geometric properties of quantum states to enhance algorithm efficiency and performance. We propose a framework for dynamically adjusting constraints based on the quantum state, enabling a more flexible and potentially more powerful optimization process. The core mechanism centers around the application of geometric constraints to guide the evolution of quantum circuits, resulting in improved results. This research explores the potential of quantum geometry in optimizing complex algorithms. The paper details the methodology, provides preliminary results demonstrating improvements in specific quantum algorithm instances, and discusses future directions for this field.

DOI: 10.5281/zenodo.22191428

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