Core Claim: Design a quantum-adaptive optimization algorithm to solve complex optimization problems, leveraging quantum properties to enhance algorithm efficiency.

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

This paper presents a novel quantum-adaptive optimization algorithm designed to address challenges in computationally intensive optimization tasks. The core concept revolves around employing quantum mechanics to simulate the optimization process, allowing for a fundamentally different approach compared to conventional optimization methods. We introduce a quantum-driven simulation framework that utilizes quantum properties to accelerate the algorithm's execution and potentially discover novel solutions. This work explores the potential of quantum computing to improve the performance of optimization problems across various domains, offering a promising alternative for scenarios where classical methods struggle. The research focuses on a specific, yet impactful, application: optimizing a complex, multi-dimensional function with intricate constraints. The algorithm's design incorporates key elements such as quantum state manipulation and entanglement to achieve improved convergence and solution quality. This paper details the theoretical underpinnings, implementation details, and preliminary experimental results demonstrating the algorithm's effectiveness. We will also discuss the challenges and future directions for this research, including exploring the scalability of the quantum-adaptive approach.

DOI: 10.5281/zenodo.22195515

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