Quantum Information Graphs for Adaptive Optimization of Artificial Intelligence

Jincheng Zhang

Published Sep 3, 2026• 0 citations

Abstract

Quantum information graphs (QIGs) offer a novel approach to representing and manipulating data, presenting significant advantages over traditional data structures for certain complex optimization problems. This paper explores the development of a quantum information graph-based adaptive optimization algorithm specifically designed to accelerate the training of artificial intelligence (AI) algorithms. We introduce a mechanism that dynamically adjusts the graph's structure during the optimization process, leveraging the graph's inherent properties to efficiently search for optimal solutions. The core claim is that this algorithm will achieve superior performance compared to existing optimization techniques, particularly for challenging AI training scenarios. This research aims to investigate the potential of QIGs to revolutionize AI training through enhanced optimization capabilities.

DOI: 10.5281/zenodo.22273616

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