Author: Denis Avetisyan
Research reveals that machine learning systems designed to improve the accuracy of quantum computers are susceptible to physical manipulation, raising concerns about the security of quantum computation.
Fault injection attacks targeting the machine learning components of quantum readout error correction systems demonstrate a pathway to manipulate computation results.
While quantum computing promises revolutionary computational power, the reliability of extracting meaningful results hinges on accurate qubit readout-a process increasingly reliant on machine learning. This work, ‘Fault Injection Attacks on Machine Learning-based Quantum Computer Readout Error Correction’, presents the first analysis of the susceptibility of these ML-driven readout error correction models to physical fault injection attacks. Our findings demonstrate that targeted voltage glitches can reliably induce mispredictions within these models, corrupting readout results in a structured, non-random manner. Does this vulnerability necessitate a fundamental shift towards security-conscious design in quantum control and readout pipelines, and what lightweight mitigation strategies can effectively protect against these emerging threats?
The Fragile Bridge: Quantum Readout and the Threat Within
Quantum computation, at its core, demands a translation between the ephemeral world of quantum states – represented by qubits – and the definitive language of classical bits. This crucial conversion is achieved through Quantum Readout Logic, a set of operations that measures a qubit’s state and outputs a corresponding $0$ or $1$. However, this readout process isn’t perfect; it’s inherently susceptible to errors stemming from noise, imperfect measurement devices, and signal degradation. Because qubits exist in superpositions and entanglement, the act of measurement itself disturbs their delicate state, creating opportunities for inaccuracies. These errors, even if seemingly minor, can propagate through a quantum algorithm, ultimately corrupting the final result and undermining the potential advantages of quantum processing. Therefore, understanding and mitigating the vulnerabilities of Quantum Readout Logic is paramount to building reliable and scalable quantum computers.
Quantum computations, despite their potential for groundbreaking calculations, are acutely vulnerable to errors introduced during the crucial process of measuring qubit states – a phenomenon known as Quantum Readout Error. These errors aren’t simply random fluctuations; they manifest as both bias and correlation. Bias errors systematically skew the measurement results, consistently misreporting a state – for example, always registering a ‘0’ when the qubit is actually in a ‘1’ state. More subtly, correlated errors arise when the readout of one qubit influences the readout of others, introducing dependencies that violate the fundamental principles of quantum mechanics and leading to incorrect computational outcomes. The presence of either type of error fundamentally compromises the integrity of the entire quantum computation, potentially rendering even the most sophisticated algorithms useless and highlighting the critical need for robust error mitigation strategies.
The fundamental operation of translating quantum information into a classically interpretable form – quantum readout – presently depends on a foundation of Trusted Classical Infrastructure. This reliance, however, introduces a critical vulnerability often overlooked in discussions of quantum security. The assumption that this classical infrastructure remains uncompromised – free from malicious actors or inherent failures – is increasingly questionable as quantum technologies mature. Current security protocols largely focus on protecting the quantum system itself, neglecting the potential for attacks targeting the classical components responsible for interpreting qubit states. A compromised classical readout system could inject false data into the computation, effectively rendering the entire quantum process untrustworthy, regardless of the sophistication of the quantum error correction employed. This presents a significant challenge, as securing the classical interface becomes paramount to ensuring the overall integrity and reliability of quantum computation.
Systematic Stress Testing: Probing Resilience Through Fault Injection
Fault injection is a proactive security testing methodology used to evaluate the error handling capabilities and overall robustness of a system. This technique involves deliberately introducing anomalies – such as unexpected inputs, memory corruption, or timing variations – into a running system to observe its response. The goal is not to simply cause failure, but to determine how the system fails, identifying vulnerabilities that could be exploited by malicious actors. By systematically injecting faults, developers and security analysts can assess whether safety mechanisms are functioning as expected, data integrity is maintained, and the system can recover gracefully from unexpected conditions. This process helps to strengthen the system against both accidental errors and deliberate attacks, leading to more reliable and secure designs.
Physical fault injection involves directly interacting with a device’s hardware while it is performing computations, enabling analysis of its security and reliability. Tools such as the ChipWhisperer Husky facilitate this interaction by providing controlled manipulation of electrical signals, specifically voltage and clock signals. This allows researchers to induce errors – such as bit flips or instruction skips – at runtime, bypassing traditional software-based security measures. The technique is used to evaluate a system’s resistance to side-channel attacks and its ability to maintain correct operation under adverse conditions, and can reveal vulnerabilities not detectable through static analysis or software testing.
Voltage glitching introduces transient voltage variations during a device’s operation, causing subtle alterations to its computational processes. These variations can manifest as bit flips, altered instruction execution, or skipped instructions, without necessarily causing a complete system failure. The induced errors are often non-destructive and temporary, allowing for repeated testing and analysis. By monitoring the system’s response to these voltage perturbations, security researchers can identify vulnerabilities related to error handling, data integrity, and control flow mechanisms. The granularity of control over voltage levels, pulse widths, and timing allows for precise fault injection, facilitating detailed analysis of the system’s resilience to a range of error conditions.
Automated Campaigns and the Machine Learning Amplifier
Optuna is an automated hyperparameter optimization framework that significantly accelerates fault injection campaigns. Traditional fault injection requires manual tuning of parameters such as voltage pulse width, current levels, and timing, which is a time-consuming and often inefficient process. Optuna automates this parameter exploration by defining a search space and employing algorithms like Tree-structured Parzen Estimator (TPE) and Bayesian Optimization to intelligently suggest parameter combinations. This allows researchers to efficiently identify optimal fault injection settings that maximize error rates or uncover vulnerabilities in quantum systems, reducing the experimental burden and increasing the scope of fault injection studies.
Modern quantum systems increasingly employ Machine Learning (ML)-based readout pipelines to enhance the fidelity of qubit measurement. These pipelines utilize Deep Neural Networks (DNNs) to analyze raw measurement data, typically in the form of In-phase and Quadrature (IQ) samples, and extract more accurate qubit state estimations. The implementation of DNNs addresses limitations in traditional signal processing techniques and improves the ability to discriminate between qubit states, particularly in the presence of noise and imperfections in the measurement apparatus. This approach allows for more reliable characterization of qubit performance and facilitates the implementation of advanced quantum algorithms; however, it introduces computational complexity and potential vulnerabilities, as the DNN itself becomes a critical component of the quantum system.
Modern quantum readout pipelines utilize Deep Neural Networks (DNNs) to analyze In-phase and Quadrature (IQ) samples, enabling error detection and correction. However, this introduces a novel attack surface as all layers within the DNN are susceptible to voltage-glitch fault injection. Experimental results indicate that manipulating the voltage supplied to any layer of the network can induce incorrect classifications of qubit states. This vulnerability stems from the DNN’s sensitivity to input perturbations, where even small voltage variations can alter neuron activation and ultimately compromise the accuracy of the quantum measurement process, potentially leading to erroneous computation results.
Fortifying the Readout: HERQULES and the Landscape of Vulnerability
HERQULES represents a significant advancement in quantum readout by offering a machine learning-based post-processing technique specifically designed for efficient hardware implementation and scalability. Unlike traditional methods that can be computationally expensive, HERQULES leverages the principles of machine learning to refine raw quantum measurement data, effectively mitigating noise and improving the accuracy of state determination. This approach is particularly valuable as quantum systems grow in complexity, demanding readout solutions that can handle increasing qubit counts without prohibitive resource demands. The architecture of HERQULES prioritizes hardware efficiency, allowing for deployment on resource-constrained platforms and facilitating the construction of larger, more powerful quantum computers. By integrating machine learning directly into the readout process, HERQULES offers a pathway towards robust and scalable quantum systems, crucial for realizing the full potential of quantum computation.
Investigations into the vulnerability of machine learning-enhanced quantum readout demonstrate the feasibility of targeted fault injection attacks focused on specific layers within the processing pipeline. By employing layer-specific timing windows and leveraging metrics such as Hamming Distance, researchers were able to selectively disrupt computation at designated points. Notably, the initial layers – specifically Dense 1 and ReLU 1 – proved most susceptible to these attacks, exhibiting a success rate of 27 out of 96 attempted faults. This suggests that the early stages of data processing represent a critical point of vulnerability, as disruption there can significantly impact the overall accuracy of the quantum readout system and necessitates focused security measures.
Analysis of the machine learning pipeline revealed a distinct vulnerability gradient, with later layers – specifically layers 3, 4, and 5 – demonstrating significantly increased resilience to targeted fault injection attacks, achieving a success rate of only 5 out of 96 attempts. Conversely, the initial layers, namely layers 1 and 2, proved considerably more susceptible, resulting in a 20% reduction in accurate output when subjected to similar attacks. This disparity underscores the critical importance of prioritizing protective measures for the early stages of quantum readout processing; securing these layers is paramount for building robust and reliable quantum systems capable of withstanding adversarial manipulation and maintaining data integrity. Understanding this vulnerability landscape allows for the development of targeted defenses and enhances the overall security posture of quantum computing infrastructure.
The study reveals a critical interplay between system components, much like a complex biological system. Vulnerabilities within the machine learning-based error correction-specifically, susceptibility to voltage glitching-demonstrate how localized interference can disrupt the entire quantum computation. This echoes a fundamental principle of interconnectedness; altering one element-the readout process-without considering the broader implications for error correction introduces systemic risk. As Paul Dirac once stated, “I have not the slightest idea of what I am doing.” This sentiment, while perhaps humorous, underscores the need for comprehensive understanding when dealing with intricate systems; a seemingly minor adjustment to the readout can cascade into significant errors, highlighting the delicate balance required for reliable quantum computation. The research emphasizes that robust security isn’t simply about patching individual flaws, but about grasping the holistic behavior of the system.
The Road Ahead
The demonstrated susceptibility of machine learning-driven quantum error correction to physical fault injection is not merely a security concern; it is a symptom. The architecture of these systems – the coupling of delicate quantum states with comparatively robust classical control and analysis – inherently introduces tension. Optimizing for one domain invariably creates new vulnerabilities in another. This work illuminates that optimization is not resolution, but displacement of the problem.
Future investigation must move beyond treating error correction as a discrete module. The system’s behavior over time – its response to subtle perturbations – dictates its true resilience. A comprehensive analysis necessitates modeling the entire stack, from the quantum substrate to the classical inference engine, recognizing that a localized ‘fix’ is likely to exacerbate instability elsewhere.
The path forward isn’t simply about more robust machine learning algorithms, or more precise fault detection. It demands a re-evaluation of the fundamental design principles governing quantum computation, acknowledging that perfect error correction is an asymptotic ideal. The question is not whether these systems can be made invulnerable, but whether their inherent vulnerabilities are acceptable within a given operational context.
Original article: https://arxiv.org/pdf/2512.20077.pdf
Contact the author: https://www.linkedin.com/in/avetisyan/
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2025-12-24 10:26