Self-Adaptive Quantum Entanglement Computing Model

Jincheng Zhang

Published Sep 5, 2026• 0 citations

Abstract

Quantum entanglement, a fundamental aspect of quantum mechanics, holds immense potential for revolutionizing computation. However, harnessing this potential requires sophisticated computational techniques, particularly in the realm of quantum information processing. This paper introduces a novel self-adaptive quantum entanglement computing model that dynamically adjusts the entangled state to optimize computational efficiency and precision. We propose a mechanism for continuous state adjustment, leveraging a feedback loop to minimize errors and maximize the utility of entangled qubits. The model's design is predicated on the principle of optimizing entanglement characteristics for specific computational tasks. The core claim of this work is that this self-adaptive approach significantly improves the performance of quantum algorithms, particularly in scenarios requiring high fidelity and reduced error rates. The paper details the model's architecture, the implemented dynamic adjustment mechanism, and preliminary experimental results demonstrating its efficacy.

DOI: 10.5281/zenodo.22318733

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