Quantum Metaphoric Computing: An Adaptive Learning Algorithm

Jincheng Zhang

Published Sep 7, 2026• 0 citations

Abstract

This paper introduces an adaptive learning algorithm for quantum metaphoric computing, aiming to automatically adjust computational models based on practical application contexts. The core of this approach leverages the unique properties of quantum metaphoric computation – the ability to represent and manipulate abstract concepts – to dynamically refine model parameters. The algorithm employs a novel learning framework centered around iterative refinement and contextual awareness, enabling the system to rapidly adapt to evolving demands. This research explores the potential for this approach to significantly enhance the efficiency and applicability of quantum metaphoric computing across diverse scenarios. The paper details the algorithm's architecture, the underlying mathematical principles, and preliminary results demonstrating its effectiveness in a simulated environment.

DOI: 10.5281/zenodo.22595666

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