Quantum Error Correction Coding via Adaptive Optimization

Jincheng Zhang

Published Aug 29, 2026• 0 citations

Abstract

This paper investigates the potential of adaptive optimization techniques for enhancing quantum error correction (QEC) coding. The core claim is that the efficiency of QEC can be significantly improved by dynamically analyzing the state of quantum bits (qubits) during the error correction process and adapting the coding scheme accordingly. Traditional QEC methods often rely on fixed encoding strategies, neglecting the evolving nature of qubit states. We propose a framework utilizing machine learning algorithms to analyze error patterns generated during the correction process. This analysis then drives a dynamic adjustment of key QEC parameters, such as redundancy and encoding structure, with the goal of maximizing correction capability. The presented approach represents a novel direction in QEC, moving away from static coding schemes towards a more responsive and intelligent system. The theoretical framework and algorithmic considerations are outlined, demonstrating the potential benefits of adaptive optimization for achieving higher fidelity quantum computations. The focus is on the conceptual development and potential implementation strategies, without the need for experimental validation.

DOI: 10.5281/zenodo.22153340

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