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Quantum science and technology

Quantum Error Correction and Detection for Quantum Machine Learning

Eromanga Adermann, Haiyue Kang, Martin Sevior, Muhammad Usman

Published Jan 1, 2026• 0 citations
Publisher
DOI
10.1007/978-3-032-11153-1_17

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At the intersection of quantum computing and machine learning, quantum machine learning (QML) is poised to revolutionize artificial intelligence. However, the vulnerability of the current generation of quantum computers to noise and computational error poses a significant barrier to this vision. Whilst quantum error correction (QEC) offers a promising solution for almost any type of hardware noise, its application requires millions of qubits to encode even a simple logical algorithm, rendering it impractical in the near term. In this chapter, we examine strategies for integrating QEC and quantum error detection (QED) into QML under realistic resource constraints. We first quantify the resource demands of fully error-corrected QML and propose a partial QEC approach that reduces overhead while enabling error correction. We then demonstrate the application of a simple QED method, evaluating its impact on QML performance and highlighting challenges we have yet to overcome before we achieve fully fault-tolerant QML.
Abstract Quantum machine learning (QML) is an emerging field that promises advantages such as faster training, improved reliability and superior feature extraction over classical counterparts. However, its implementation on quantum hardware is challenging due to the noise inherent in these systems, necessitating the use of quantum error correction (QEC) codes. Current QML research remains primarily theoretical, often assuming noise-free environments and offering little insight into the integration of QEC with QML implementations. To address this, we investigate the performance of a simple, parity-classifying Variational Quantum Classifier (VQC) implemented with the [[4,2,2]] error-detecting stabiliser code in a simulated noisy environment. To our knowledge, this is the first implementation of a stabiliser-based error detection code to a QML algorithm. We invoke ancilla qubits to logically encode rotation gates, and classically simulate the logically-encoded VQC under two simple noise models representing gate noise and environmental noise. We demonstrate that the stabiliser code improves the training accuracy at convergence compared to noisy implementations without error detection. However, we find that the effectiveness and reliability of error detection is contingent upon keeping the ancilla qubit error rates below a specific threshold, due to the propagation of ancilla errors to the physical qubits. Our results provide an important insight that extends to QML implemented with full error-correction: when the QEC code requires ancilla qubits for logical rotations but cannot fully correct errors propagated between ancilla and physical qubits, the maximum achievable accuracy of the QML model is constrained. This highlights the need for additional error correction or mitigation strategies to support the practical implementation of QML algorithms with QEC on quantum devices.
This paper explores the potential of leveraging quantum error correction (QEC) codes to enhance distributed machine learning (DML) optimization. The core claim is that QEC's inherent noise mitigation capabilities can significantly improve the robustness and efficiency of DML model training. The proposed mechanism involves encoding machine learning model parameters into quantum states, utilizing QEC for inter-node communication and computation, and concurrently performing error detection and correction. This represents a novel approach, as QEC and DML have historically been pursued independently. The research investigates the theoretical underpinnings of this integration, focusing on key aspects such as code selection, communication protocols, and error correction strategies within the distributed environment. We examine the impact of varying noise levels and communication constraints on the overall performance of the DML system. The ultimate goal is to demonstrate a framework where QEC's capabilities can be directly translated into a more reliable and scalable DML solution.