Title: Adaptive Quantum Entanglement Optimization for Quantum Algorithms

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

Quantum algorithms hold immense promise for scientific discovery and technological advancement, but their practical implementation often faces significant challenges related to efficiency and accuracy. This paper presents a novel quantum algorithm optimization framework based on the concept of adaptive quantum entanglement. We propose a "Quantum Entanglement Parameter Optimizer" (QEPO) that dynamically adjusts entanglement parameters to enhance the performance of quantum algorithms, particularly in the domains of quantum simulation and quantum computation. The core mechanism of the QEPO leverages the inherent properties of quantum entanglement to automatically optimize these parameters, leading to improved results. The study demonstrates the effectiveness of this approach through simulations and preliminary experiments, highlighting its potential to revolutionize quantum algorithm optimization.

DOI: 10.5281/zenodo.22200973

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