Benchmarking Machine Learning Models for Quantum Error Correction - Comprehensive benchmark and long-range dependency evaluation

Aug 2, 2026

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Quantum Error Correction (QEC) detects and corrects errors in data qubits, enabling reliable quantum computation despite unstable hardware, environmental noise, imperfect control, and unpredictable interactions. Existing learning-based approaches often focus on local patterns and struggle to capture long-range dependencies inherent in QEC. This study introduces a curated machine-learning benchmark for QEC, evaluates seven deep-learning architectures, and uses experiments to show that enlarging the receptive field—leveraging distant ancilla information—substantially improves accuracy.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages19

Reading Time48 min

AccessFree to download

Abstract

1 Introduction

2 Background

2.1 Quantum Basics

arXiv :2311 . 11167v3 [ quant-ph] 4 Apr 2024 Benchmarking Machine Learning Models for Quantum Error Correction Yue Zhao Department of Computer Science, University of Southern California April 8, 2024 Abstract Quantum Error Correction (QEC) is one of the fundamental problems in quantum computer systems, which aims to detect and correct errors in the data qubits within quantum computers. Due to the presence of unreliable data qubits in existing quantum computers, implementing quantum error correction is a critical step when establishing a stable quantum computer system. Recently, machine learning (ML)-based approaches have been proposed to address this challenge. However, they lack a thorough understanding of quantum error correction. To bridge this research gap, we provide a new perspective to understand machine learning-based QEC in this paper. We find that syndromes in the ancilla qubits result from errors on connected data qubits, and distant ancilla qubits can provide auxiliary information to rule out some incorrect predictions for the data qubits. Therefore, to detect errors in data qubits, we must consider the information present in the long-range ancilla qubits. To the best of our knowledge, machine learning is less explored in the dependency relationship of QEC. To fill the blank, we curate a machine learning benchmark to assess the capacity to capture long-range dependencies for quantum error correction. To provide a comprehensive evaluation, we evaluate seven state-of-the-art deep learning algorithms spanning diverse neural network architectures, such as convolutional neural networks, graph neural networks, and graph transformers. Our exhaustive experiments reveal an enlightening trend: By enlarging the receptive field to exploit information from distant ancilla qubits, the accuracy of QEC significantly improves. For instance, U-Net can improve CNN by a margin of about 50% . Finally, we provide a comprehensive analysis that could inspire future research in this field. 1 Introduction Quantum computing [26, 17 , 36] is one of the most promising techniques in both computer science and physics. Once fully realized, it can change various fields such as cryptography [9], material science [22], complex system simulations [8], and more. The most significant advantage of quantum computers over traditional computers is their superior computing power, which increases exponentially with the number of qubits [15] . However, realizing practical and large-scale quantum computers still faces several challenges. Among them, Quantum Error Correction (QEC), aiming to detect and correct errors in data qubits within quantum systems, remains a significant concern. Unlike bit errors in classical computers, quantum errors are more complex [29, 28] and arise from varied sources, including environmental noise, imprecise qubit control, and unpredictable qubit interactions. These errors can introduce some inaccuracies in quantum computations, decreasing the overall reliability of the results. Traditional non-data-driven quantum error correction methods, such as the minimum weight perfect matching (MWPM) [7], encounter scalability challenges on larger quantum systems [35] . It is important for error correction schemes to detect errors efficiently within a limited time budget to guarantee quantum computing’s practical application. Furthermore, as quantum computers expand in scale, these schemes should work on more quantum bits. Given its computational complexity, MWPM fails to be a choice for QEC in larger quantum computers [4] . On the other hand, recent advancements have introduced datadriven QEC schemes, which employ neural network architectures like multilayer perceptron (MLP) [6] or convolutional neural networks (CNN) [4] and demonstrate initial success in QEC. However, these machine learning methods are restricted to capture local patterns and cannot model long-range dependency in qubits of quantum systems. This work fo

FAQs

What problem does Quantum Error Correction address in quantum computers?

Quantum Error Correction aims to detect and correct errors in data qubits so quantum computations remain reliable despite hardware noise and control imperfections.

Why do many ML-based QEC methods struggle?

They are often limited to capturing local patterns, making it difficult to model the long-range dependencies among qubits that influence correct error detection.

How does this paper improve QEC performance using machine learning?

It introduces a benchmark and shows that increasing the receptive field to exploit information from distant ancilla qubits significantly improves QEC accuracy, with examples like U-Net improving over CNN.

Aug 2, 2026

Upload by Liam