Quantum Error Correction Protocols: A Topology-Enhanced Reinforcement Learning Approach

Jincheng Zhang

Published Sep 7, 2026• 0 citations

Abstract

Quantum error correction (QEC) is a critical component of quantum computing, enabling reliable transmission and manipulation of quantum information. Current QEC protocols, particularly surface codes, suffer from inherent vulnerabilities to information loss due to noise and imperfections in the hardware. This paper introduces a novel quantum error correction protocol utilizing topology-enhanced reinforcement learning (TEL) to mitigate these risks. The core concept is to model the quantum error correction process as a dynamically evolving topological structure, effectively reducing the vulnerability to information leakage. We propose a TEL-based algorithm that leverages the inherent properties of topological spaces to guide the optimization process, leading to improved error correction capabilities. This research presents a preliminary investigation of the algorithm's effectiveness through simulations and a simplified, illustrative example. The presented approach offers a promising pathway towards robust and efficient QEC, potentially simplifying the development of practical quantum systems.

DOI: 10.5281/zenodo.22636658

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