Quantum-Enhanced Optimization with Sparse Matrices

Jincheng Zhang

Published Sep 3, 2026• 0 citations

Abstract

Quantum-enhanced optimization aims to accelerate the solution of complex optimization problems by utilizing quantum mechanics to improve the efficiency of algorithms. This paper investigates a novel quantum optimization algorithm specifically designed to handle sparse matrices, a common challenge in many real-world applications. We demonstrate the potential of quantum properties – particularly entanglement and superposition – to significantly reduce computational complexity and improve solution speed compared to classical approaches. The core mechanism centers around exploiting quantum algorithms to efficiently navigate and process sparse data structures, leading to substantial performance gains. This work offers a promising pathway to address a critical bottleneck in optimization, with implications for diverse fields including machine learning, finance, and logistics.

DOI: 10.5281/zenodo.22278201

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