Quantum Machine Learning: A Novel Approach to Variable Tensor & Activation Function

Jincheng Zhang

Published Sep 7, 2026• 0 citations

Abstract

Quantum Machine Learning (QML) is a rapidly evolving field exploring the potential of quantum computing to revolutionize machine learning. This paper introduces a novel quantum machine learning algorithm, termed "Variable Tensor & Activation Function" (VTAF), which leverages the unique properties of variable tensors and activation functions to enhance data representation and improve machine learning performance. We propose a new algorithm architecture based on the integration of these components, aiming to overcome limitations of existing quantum machine learning approaches. The core claim of this work is that VTAF can effectively capture complex relationships within data, resulting in improved performance across a range of machine learning tasks. The algorithm's design incorporates quantum entanglement and superposition to facilitate the exploration of high-dimensional spaces, leading to enhanced feature extraction and pattern recognition. We detail the algorithm's architecture, demonstrate its effectiveness through illustrative examples, and discuss potential future research directions.

DOI: 10.5281/zenodo.22636323

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