Quantum-Based Non-linear Adaptive Learning

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

Quantum-Based Non-linear Adaptive Learning is a novel machine learning framework designed to address the challenges of complex data analysis, particularly in scenarios involving non-linear relationships. This paper introduces a new approach leveraging the unique properties of quantum tensors to model data distributions non-linearly and employ adaptive learning strategies. The core mechanism centers around utilizing quantum tensor representations to capture intricate patterns and relationships within the data, allowing for significantly faster learning and prediction compared to traditional methods. We provide a comprehensive theoretical analysis and preliminary implementation demonstrating the framework's efficacy in handling complex datasets. This work represents a significant advancement in learning paradigms, offering the potential for more robust and efficient data analysis.

DOI: 10.5281/zenodo.22201489

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