Quantum-Guided Optimization for Sparse Data Representation

Jincheng Zhang

Published Sep 7, 2026• 0 citations

Abstract

Quantum-Guided Optimization (QGO) presents a novel approach to optimizing sparse data representations by utilizing quantum entanglement to identify and leverage key features. This paper explores the potential of quantum mechanics to enhance the efficiency of optimization processes, specifically targeting the challenges presented by sparse data. We detail the core mechanism of QGO, outlining the quantum entanglement process designed to pinpoint critical patterns within the data. The research investigates the advantages of this methodology over traditional optimization techniques and proposes a robust framework for achieving superior results with reduced computational complexity. The paper concludes with a discussion of the implications of this research and potential future directions.

DOI: 10.5281/zenodo.22638783

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