Hardware Fingerprinting FTQC via Quantum Decoder Timing
Quantum decoder timing on IBM Heron processors forms a side channel enabling device fingerprinting with 89% accuracy and workload inference.
The work demonstrates that wall-clock syndrome-decoding times on fault-tolerant quantum computers constitute a novel hardware side channel. Using per-shot decoder timings from three IBM Heron processors collected over 68 days, a passive observer can reconstruct detector-firing distributions, estimate logical error rate, infer code distance, and fingerprint the specific device with up to 89% accuracy versus 33% for random guessing. Noisy simulation based on Google's 105-qubit Willow processor distinguishes nine surface-code patches at 81% accuracy, showing the channel persists across vendors and code families.
- Decode-time distributions fingerprint IBM Heron devices with up to 89% accuracy
- Side channel passively reveals logical error rate and code distance
- Persists in simulation on Google Willow with 81% patch-level accuracy
Full article205 words · extracted from arxiv.org · click to collapse
As the quantum computing field transitions toward Fault-Tolerant Quantum Computing (FTQC), intensive efforts are focused on scaling architectures and realizing active error correction. However, this shift introduces security surfaces that remain largely unexplored. Fault-tolerant quantum computers pair a quantum processor with a classical decoder that sits on the critical path of every syndrome-extraction round. For the first time, this work demonstrates that the wall-clock time each decoder takes to process a syndrome measurement and decoding round constitutes a novel, exploitable hardware side channel on physical quantum hardware. Using per-shot decoder timings from three IBM Heron processors collected over a 68-day window, the decode-time distribution alone allows a passive observer to (i) reconstruct the shot-by-shot detector-firing distribution and estimate the workload's logical error rate $p_L$, (ii) infer the code distance in use, and (iii) fingerprint the specific physical device with up to 89% accuracy (a random guess is 33%), with a pooled two-sample Kolmogorov-Smirnov test confirming the decode-time distributions are statistically distinct. In noisy simulation inspired by public data from Google's 105-qubit Willow processor, decoder timing further distinguishes 9 surface-code patches at different locations on the chip with 81% accuracy, showing the side channel persists on below-threshold fault-tolerant hardware from a different vendor and code family.
Text extracted automatically; images, tables and formatting may be missing. Original: https://arxiv.org/abs/2609.12145