Machine-learning (ML) models are increasingly used in quantum computing systems to discriminate multi-qubit readouts, mitigate correlated readout errors, and decode quantum error-correcting codes, making them an integral component of today's quantum computer control and readout stacks. This paper is the first to analyze the susceptibility of such ML models to physical fault injection, which can cause quantum computers to return incorrect results or perform wrong error correction operation. This work studies two representative architectures: (i) a fully connected neural network for 5-qubit (32-class) readout error correction (HERQULES), and (ii) a convolutional neural network used as a Deep Q-learning (Deep Q) decoder for the distance-5 Surface Code. Using the ChipWhisperer Husky for voltage glitching together with automated search over the fault parameter space, this work localizes successful fault settings to specific layers of each target ML model. On the HERQULES model, fault susceptibility is strongly layer-dependent: early layers exhibit higher misprediction rates than later layers. On the Deep Q decoder, a single trigger-aligned voltage glitch in either the first convolutional layer or the final fully connected output layer reduces decoding accuracy from 100% to as low as 21.57%. We further characterize the resulting failures at the bitstring level using Hamming-distance and per-bit flip statistics, showing that single-shot glitches can induce structured corruption rather than purely random noise. These results motivate treating ML-based quantum readout and error-correction decoding as security-critical components, and highlight the need for lightweight fault-detection and redundancy mechanisms in quantum computing pipelines.
Machine-learning (ML) models are increasingly used in quantum computing systems to discriminate multi-qubit readouts, mitigate correlated readout errors, and decode quantum error-correcting codes, making them an integral component of today's quantum computer control and readout stacks. This paper is the first to analyze the susceptibility of such ML models to physical fault injection, which can cause quantum computers to return incorrect results or perform wrong error correction operation. This work studies two representative architectures: (i) a fully connected neural network for 5-qubit (32-class) readout error correction (HERQULES), and (ii) a convolutional neural network used as a Deep Q-learning (Deep Q) decoder for the distance-5 Surface Code. Using the ChipWhisperer Husky for voltage glitching together with automated search over the fault parameter space, this work localizes successful fault settings to specific layers of each target ML model. On the HERQULES model, fault susceptibility is strongly layer-dependent: early layers exhibit higher misprediction rates than later layers. On the Deep Q decoder, a single trigger-aligned voltage glitch in either the first convolutional layer or the final fully connected output layer reduces decoding accuracy from 100% to as low as 21.57%. We further characterize the resulting failures at the bitstring level using Hamming-distance and per-bit flip statistics, showing that single-shot glitches can induce structured corruption rather than purely random noise. These results motivate treating ML-based quantum readout and error-correction decoding as security-critical components, and highlight the need for lightweight fault-detection and redundancy mechanisms in quantum computing pipelines.
Machine learning (ML) decoders are increasingly used in quantum error correction (QEC) to model noise, analyze imperfect syndrome measurements, and replace iterative matching algorithms with a learned inference pass, making them a critical component of the classical control stack of a fault-tolerant quantum computer. Prior security analysis of the ML decoders in the controller stack has addressed integrity and fault-injection attacks on the decoders. This work is the first to explore physical power side-channel attacks on ML decoders. In particular, it studies a convolutional Deep Q-Learning decoder for the distance-5 surface code. This work profiles the decoder to capture the power signatures generated during classification tasks and trains a classifier to identify which qubit the decoder identified as having errors based on the power signatures. The trained classifier is then applied to test data, where it recovers the correct data qubit label from the decoder’s side-channel data with up to 83.2% accuracy across 25 classes, against 4% for random guessing. A second classifier is trained to identify the type of error labeled by the decoder, Pauli X or Z, and reaches 84.6% accuracy, against 50% for random guessing. This work further performs an initial study of a timing channel, which complements the power side channels, exploring how the duration of the decoder’s first convolutional layer can leak the code size for which the decoder is compiled. All the side-channel attacks demonstrated in this work motivate treating the embedded QEC decoder as a confidentiality-critical component of the quantum control stack.
Decoding is an essential component of quantum error correction (QEC), translating stabilizer measurement outcomes into corrective actions that suppress logical errors and preserve logical quantum information. Building fault-tolerant architectures requires increasing the code distance, which in turn places growing demands on decoding accuracy, scalability, and practical deployability. While a wide range of decoding algorithms have been proposed and demonstrated, achieving reliable, scalable, and real-time decoding remains a significant challenge. Machine-learning (ML) approaches are particularly well suited to this setting, as quantum error decoding is fundamentally a problem of processing large volumes of classical data with complex spatiotemporal correlations. This chapter surveys ML-based methods for quantum error decoding, with a focus on topological codes and an emphasis on architectural principles, practical performance, and real-time considerations. We first frame decoding as a learning problem and outline key paradigms, including discriminative, generative, and reinforcement-learning formulations. We then introduce the neural network building blocks that underpin most contemporary neural decoders and discuss how these components can be integrated to balance expressivity, scalability, and latency. Building on this architectural perspective, we review recent progress and benchmarks in neural decoding for memory experiments, and discuss real-time decoding, open challenges, and future directions toward scalable fault-tolerant quantum computing.
Neural network decoders are essential for fault-tolerant quantum computation, yet their internal mechanisms remain poorly understood. Leading machine learning decoders use recurrent and transformer models (e.g., AlphaQubit), and reinforcement learning (RL) is increasingly used to train such models. In this work we ask whether an RL surface code decoder, DeepQ, can be made to fail systematically. Our white-box search repeatedly draws candidate syndrome volumes from the unmodified noise model and retains the one the decoder's learned Q-function is most pessimistic about. At code distance 5 under phenomenological depolarising noise, it cuts the average logical qubit lifetime from roughly 105 cycles to 60. We quantify what this costs: the retained error histories are individually plausible, but the selected trajectory is roughly a 10−18 tail event under undisturbed noise, so the effect is bought entirely by the ability to select among physically realisable error configurations. Controls confirm this is not an artefact of a weak target: the decoder resists noise fluctuations, tolerates an MWPM referee, and is fault tolerant at checkable distances. The result is a robustness upper bound for one decoder under one noise model, but to our knowledge the first adversarial attack of a machine learning QEC decoder.
Quantum computation has the promise to solve problems that are not efficiently solvable by classical means. Yet, due to the fragile nature of quantum states, it is unavoidable that quantum systems are subject to decoherence, gate imperfections, and measurement errors. These problems stand as a major roadblock to the construction of large-scale fault-tolerant quantum computers. Conventional quantum error correction (QEC) codes, such as the surface code or Shor code, need to use an extensive amount of redundancy and classical control, which can be both computationally expensive and challenging to scale. In this context, we propose to investigate the use of machine learning (ML) techniques by realizing on-line identification, localization and correction of these quantum errors for fault-tolerant quantum computation by means of fault-tolerant quantum computing processes. Our model introduces a hybrids ML-QEC architecture that combines classical neural networks and decision-tree classifiers with quantum syndrome decoding. These models are trained on error syndromes produced using the approximation of common noise models (for example, depolarizing noise, dephasing and amplitude damping), simulated under different QEC schemes. The machine learning methods are trained with quantum error patterns and have a capability to rapidly infer where errors are likely to occur, as well as recommend correction operations with high confidence. As a result, the system reduces dramatically the delay and computational costs when compared to conventional decoding methods. There is also a self-adaptive learning module in the work, which recommences training on fresh quantum error distributions, meaning the system can adapt to changes in the environment (and also the decay of the hardware) as time progresses. The study further carries out ML predictions inside a real-time loop and cross-checks them with quantum state tomography and entanglement fidelity such as indicators. Experimental characterization on dedicated devices as well as noise models show that our ML-enhanced decoders reach higher fidelity and lower logical error rates compared to baseline decoders, especially in non-uniform or time-dependent noise. This work is a significant stride towards realizing practical fault-tolerant quantum computation by combining quantum error correction with the versatility and scalability of machine learning. With intelligent models to dictate learning, our proposed framework is able to eliminate a lot of trial-and-error manual processes in the conventional heuristic-based approach, and points to a potential path to achieve scalable quantum information processing platforms.
A fault-tolerant quantum computation requires an efficient means to detect and correct errors that accumulate in encoded quantum information. In the context of machine learning, neural networks are a promising new approach to quantum error correction. Here we show that a recurrent neural network can be trained, using only experimentally accessible data, to detect errors in a widely used topological code, the surface code, with a performance above that of the established minimum-weight perfect matching (or blossom) decoder. The performance gain is achieved because the neural network decoder can detect correlations between bit-flip (X) and phase-flip (Z) errors. The machine learning algorithm adapts to the physical system, hence no noise model is needed. The long short-term memory layers of the recurrent neural network maintain their performance over a large number of quantum error correction cycles, making it a practical decoder for forthcoming experimental realizations of the surface code.
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.