Quantum Entanglement-Driven Algorithmic Complexity Reduction

Jincheng Zhang

Published Sep 4, 2026• 0 citations

Abstract

Quantum entanglement, a bizarre yet powerful quantum phenomenon, offers a tantalizing prospect for revolutionizing computationally intensive tasks. This research explores the potential of utilizing entanglement to dramatically reduce the computational complexity of complex optimization problems, with a particular focus on combinatorial optimization and machine learning. We propose a novel algorithm, guided by quantum entanglement, that employs a modified Grover's algorithm enhanced with quantum entanglement to achieve exponential speedups. The core mechanism centers around creating entangled qubit states to represent potential solutions and enabling efficient search and evaluation. This paper details the theoretical framework, algorithm design, and preliminary results demonstrating the potential of this approach. The implications of this work extend beyond simple speedups, potentially opening avenues for fundamentally new algorithmic paradigms.

DOI: 10.5281/zenodo.22289902

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