Researchers at Yale University and Northwestern University have unveiled significant vulnerabilities in machine learning (ML) systems used in quantum computer readout error correction. Their study, the first of its kind, demonstrates how physical fault injection attacks can compromise the reliability of these ML-based systems that are increasingly critical to quantum computing operations. By employing voltage glitches to disrupt the functioning of ML components, the team found that attackers could manipulate measurement results, posing serious security risks to quantum architectures.
Quantum computing relies heavily on the precise extraction of information from qubits, a process that is prone to errors. The integration of ML techniques has been pivotal in correcting these errors. However, the potential for attackers to exploit weaknesses in these ML components remains largely unexamined. The researchers, led by Anthony Etim and Jakub Szefer, utilized an automated optimization framework to investigate the vulnerability of different layers within the ML model, successfully inducing mispredictions that point to a critical security gap.
During their experiments, the team targeted a 5-qubit model tasked with distinguishing between 32 distinct readout classes. Using the ChipWhisperer Husky platform, they introduced carefully timed voltage disruptions that led to erroneous outputs in the ML model responsible for error correction. The findings revealed a clear layer dependency in fault susceptibility, with earlier layers proving to be more vulnerable to induced faults compared to later layers. This suggests that the initial stages of processing are particularly sensitive to transient errors, allowing attackers to potentially steer corrected readouts toward specific, biased patterns.
The study underscored that the faults created predictable patterns in the output rather than merely random noise, raising alarms over the security implications for quantum computations. With compromised readout logic, the integrity of quantum computations could be significantly undermined. The researchers characterized the corrupted readout data at the bitstring level, using metrics like Hamming-distance to demonstrate that even single-shot glitches led to structured corruption of data—highlighting the importance of effective readout mechanisms.
In response to these vulnerabilities, the researchers proposed several lightweight defensive strategies that could be integrated into quantum computer control systems. Suggestions include executing multiple inferences with majority voting, comparing ML outputs against simpler baseline models, monitoring for anomalies in metrics such as logits and activations, and introducing jitter to layer execution to minimize synchronization issues. These measures aim to fortify quantum readout systems against potential attacks while remaining feasible for implementation.
The research lays a foundational understanding of the security challenges faced by ML-enhanced quantum computing systems. As the technology continues to advance, the integration of robust fault detection and redundancy mechanisms will be essential to ensure the reliable operation of quantum architectures. The findings call for a re-evaluation of how security is built into the design and deployment of ML components in quantum computing, emphasizing that these elements should be treated as security-critical.
This pioneering study not only reveals the vulnerabilities inherent in ML-based quantum readout systems but also sets the stage for future inquiries into alternative fault injection methods. The implications of these findings are profound, signaling the need for a heightened focus on security measures within the rapidly evolving landscape of quantum technology.
👉 More information
🗞 Fault Injection Attacks on Machine Learning-based Quantum Computer Readout Error Correction
🧠 ArXiv: https://arxiv.org/abs/2512.20077
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