Quantum-Inspired Compression of High-Dimensional Data using Tensor Decomposition

Jincheng Zhang

Published Sep 7, 2026• 0 citations

Abstract

This paper explores a novel approach to high-dimensional data compression by leveraging concepts from quantum mechanics, specifically entanglement and superposition, within the framework of tensor decomposition. Traditional tensor decomposition methods, while effective for dimensionality reduction and feature extraction, often struggle with the computational complexity inherent in large-scale datasets. Our approach introduces a modified tensor decomposition algorithm guided by quantum-inspired principles, aiming to enhance compression efficiency and potentially improve representation accuracy. The core idea is to mimic the correlations observed in quantum entanglement to inform the iterative process of tensor factorization. We outline the theoretical foundations, detail the proposed algorithm, and discuss the potential benefits and challenges of this hybrid approach. The primary contribution lies in the integration of quantum-inspired concepts into a practical tensor decomposition method, opening new avenues for data compression in high-dimensional scenarios.

DOI: 10.5281/zenodo.22585056

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