Quantum Entanglement-Based Information Compression and Multi-Variable Functional Learning

Jincheng Zhang

Published Sep 4, 2026• 0 citations

Abstract

This paper presents a novel approach to information compression and multi-variable functional learning leveraging the unique properties of quantum entanglement. The core idea is to utilize entangled states for efficient data representation, effectively reducing redundancy and enabling accurate estimation of multi-variable functions. We propose a framework where multi-variable data is encoded into entangled states, facilitating a significant reduction in storage requirements while simultaneously providing a robust method for learning complex relationships. The theoretical underpinnings rely on the superposition and correlation properties inherent in entangled systems. The system is mathematically formulated using tensor networks and density matrix representations to describe the entangled state. The key contribution lies in the combined application of entanglement-based compression and the subsequent use of this compressed representation for learning multi-variable functions. The proposed method offers potential advantages over classical approaches, particularly in scenarios dealing with high-dimensional data and complex dependencies. The presented framework provides a foundation for future research in quantum machine learning and data analysis.

DOI: 10.5281/zenodo.22292588

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