Title: Adaptive Quantum Algorithm for Non-Linear Optimization

Jincheng Zhang

Published Sep 4, 2026• 0 citations

Abstract

This paper introduces an innovative adaptive quantum algorithm designed to address the limitations of traditional optimization techniques, particularly when dealing with non-linear problems. The algorithm leverages reinforcement learning to dynamically adjust its parameters based on the input data, offering a significant improvement over static optimization methods. We present a detailed architecture, including the core mechanism and initial implementation, demonstrating its effectiveness in a range of non-linear optimization scenarios. The goal is to provide a robust and efficient solution for complex optimization challenges across diverse fields.

DOI: 10.5281/zenodo.22302609

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