Quantum Machine Learning: Adaptive Tensor Decomposition

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

Quantum machine learning represents a paradigm shift in computational capabilities, offering the potential to solve complex problems currently intractable for classical algorithms. This paper explores the application of adaptive tensor decomposition as a novel method to enhance quantum machine learning models. We hypothesize that dynamically adjusting the weight matrices within a quantum neural network through adaptive tensor decomposition can significantly improve model accuracy and efficiency. The core mechanism focuses on a novel algorithm that adjusts the magnitude of each tensor component based on the training process, leading to a more robust and adaptable learning framework. This work details the theoretical foundations, implementation of the adaptive tensor decomposition algorithm, and preliminary experimental results demonstrating its potential to surpass existing classical machine learning approaches in certain benchmark datasets. The findings suggest a significant advancement in quantum machine learning model design and training.

DOI: 10.5281/zenodo.22197701

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