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Quantum Error Correction Protocol Adaptive Enhancement Algorithm

Jincheng Zhang

Published Sep 4, 2026• 0 citations
Publisher

Abstract

Quantum error correction (QEC) is a critical component of quantum computing, enabling the reliable transmission and manipulation of quantum information. However, current QEC protocols often rely on static parameters, which can limit their performance in complex and dynamic environments. This paper presents an innovative adaptive reinforcement algorithm for quantum error correction, designed to dynamically adjust qubit arrangements and phases based on error patterns. Our approach leverages a "quantum state mapping" mechanism to automatically optimize the correction process, enhancing both error correction efficiency and system stability. The core claims demonstrate the potential of this algorithm to surpass traditional methods through adaptive optimization.

DOI
10.5281/zenodo.22296330

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This paper presents a novel adaptive optimization algorithm for quantum error correction (QEC) codes, designed to significantly enhance the resilience of quantum computations against errors. The core claim is that by dynamically adjusting QEC parameters based on real-time analysis of error characteristics, we can achieve a substantial improvement in error correction performance. This is achieved through the integration of quantum error models and machine learning techniques, specifically reinforcement learning, enabling the algorithm to learn and adapt to varying error distributions. Traditional QEC code design often relies on static parameters, limiting their effectiveness in handling the inherent noise and fluctuations within quantum systems. This research addresses this limitation by introducing a dynamic, self-optimizing approach. The algorithm's ability to respond to evolving error profiles represents a critical step towards practical, fault-tolerant quantum computing. The proposed method focuses on optimizing code parameters such as parity checking rates, code distance, and decoding strategies to minimize the impact of observed errors. The simulation results demonstrate a notable improvement in error correction rates compared to static code implementations.