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Quantum Entanglement Computation: Adaptive Simulation

Jincheng Zhang

Published Aug 31, 2026• 0 citations
Publisher

Abstract

Quantum entanglement, a cornerstone of quantum mechanics, promises revolutionary computational capabilities. Traditional computer simulations often struggle to accurately model complex quantum systems due to the inherent limitations of classical physics. This paper introduces a novel adaptive simulation framework based on quantum entanglement, aiming to overcome these limitations by dynamically adjusting quantum system parameters during computation. We demonstrate a significant improvement in accuracy and computational efficiency compared to existing classical simulation techniques. This work represents a significant step toward realizing the full potential of quantum computation.

DOI
10.5281/zenodo.22193132

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This paper introduces a novel quantum computing framework centered around the concept of self-adaptive quantum entanglement. Traditional quantum algorithms often struggle with complex systems requiring nuanced entanglement dynamics. This framework dynamically adjusts entanglement parameters to mimic the behavior of quantum systems, offering a pathway to simulation and optimization of quantum computations. The core mechanism leverages a self-learning process to optimize entanglement, leading to enhanced performance in specific quantum scenarios. We demonstrate the framework's effectiveness through simulations showcasing improved results compared to existing approaches. This work represents a significant step toward creating more versatile and efficient quantum computation tools.
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