Quantum Entanglement Probability Optimization

Jincheng Zhang

Published Aug 29, 2026• 0 citations

Abstract

This paper investigates the use of quantum entanglement probability as a novel optimization technique for quantum computing. Traditional quantum algorithm design relies heavily on classical optimization methods, often limited by computational constraints. We introduce a new framework, leveraging the inherent probabilistic nature of quantum entanglement, to dynamically adjust quantum parameters, potentially enhancing both efficiency and precision. The core mechanism involves constructing a probabilistic model of quantum circuit parameters, where entanglement properties are integrated to guide parameter selection. This approach offers a potentially transformative path toward optimizing quantum computation, addressing limitations of existing algorithms and opening new avenues for exploration in quantum information processing. The paper demonstrates preliminary results through a simplified quantum circuit optimization scenario.

DOI: 10.5281/zenodo.22152621

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