Quantum-Inspired Entangled Representation Learning for High-Dimensional Data

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

This paper introduces a novel representation learning technique, termed "Entangled Representation Learning," which addresses the limitations of conventional dimensionality reduction methods when dealing with high-dimensional data. Traditional approaches often struggle to capture complex, non-local dependencies within the data, leading to information loss and suboptimal representations. Our method draws inspiration from quantum entanglement, explicitly modeling and encouraging correlations between data points through a modified quantum phase estimation algorithm. This process maps data into a Hilbert space, fostering 'entanglement' between distant points and generating a compressed representation that surpasses the capabilities of classical correlation matrices. We demonstrate the efficacy of this approach through a theoretical analysis and provide a detailed formulation of the algorithm, highlighting its potential for improved feature extraction and data analysis in scenarios with intricate correlations. The core claim of this work is that classical dimensionality reduction methods fail to adequately capture the complex correlations present in high-dimensional data, particularly when considering non-local dependencies. The core mechanism is the development of this novel technique, which actively encourages entanglement between distant points.

DOI: 10.5281/zenodo.22187987

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