##基于信息几何学的量子机器学习算法设计

Jincheng Zhang

Published Sep 6, 2026• 0 citations

Abstract

This paper explores a novel approach to quantum machine learning (QML) algorithm design by leveraging the principles of information geometry. The core idea is to map QML algorithms onto a high-dimensional manifold defined by information geometry, and then utilize the optimization techniques derived from this geometry to directly improve the algorithm's performance. This represents a shift from traditional QML approaches, which often rely on heuristic optimization methods, towards a more principled and potentially more efficient strategy. We present a framework for this mapping and outline a methodology for applying information geometric optimization to QML tasks. The potential benefits of this approach, including improved convergence and enhanced performance, are discussed, alongside the challenges and future research directions.

DOI: 10.5281/zenodo.22453263

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