Dynamic Quantum Entanglement for Probabilistic Computation

Jincheng Zhang

Published Sep 2, 2026• 0 citations

Abstract

Quantum entanglement, a fundamental quantum phenomenon, offers a unique pathway toward computational advancements. This paper explores the integration of dynamic quantum entanglement into probabilistic computation, aiming to enhance accuracy and efficiency in high-dimensional quantum computations and quantum machine learning. We propose a novel method leveraging entanglement's inherent properties to dynamically modulate probability distributions, offering a potential shift from static approaches. The core mechanism involves introducing controlled entanglement dynamics within a probabilistic framework, leading to improved results and offering new avenues for exploring quantum computation. This work presents a theoretical framework and preliminary experimental investigation, demonstrating the potential of this approach.

DOI: 10.5281/zenodo.22241587

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