Title: Modular Quantum Error Correction with Adaptive Thresholds

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

Quantum information processing, while promising, is inherently susceptible to errors due to the fragile nature of quantum states. Quantum error correction (QEC) is crucial for achieving reliable quantum computation, but existing protocols often suffer from limitations in their robustness. This paper explores a novel approach to QEC utilizing adaptive thresholding based on observed error patterns, aiming to significantly improve resilience to noise. We propose a dynamic threshold adjustment mechanism that learns a 'noise profile' and adapts the threshold accordingly, providing a more effective and robust solution than traditional fixed thresholds. The core mechanism hinges on a self-learning algorithm that analyzes the quantum state and its evolution to determine the optimal threshold for error detection and correction. This research addresses a fundamental challenge in QEC – the need for adaptive thresholds in the presence of complex noise – and presents a framework for enhancing the overall error correction capabilities of quantum systems. The theoretical underpinnings and implementation details are detailed within the paper.

DOI: 10.5281/zenodo.22199792

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