Calibration-Conditioned FiLM Decoders for Low-Latency Decoding of Quantum Error Correction Evaluated on IBM Repetition-Code Experiments

• URL: http://arxiv.org/abs/2601.16123v1
• Date: Thu, 22 Jan 2026 17:23:32 GMT
• Title: Calibration-Conditioned FiLM Decoders for Low-Latency Decoding of Quantum Error Correction Evaluated on IBM Repetition-Code Experiments
• Authors: Samuel Stein, Shuwen Kan, Chenxu Liu, Adrian Harkness, Sean Garner, Zefan Du, Yufei Ding, Ying Mao, Ang Li,
• Abstract summary: Real-time decoding of quantum error correction (QEC) is essential for enabling fault-tolerant quantum computation.<n>We introduce a hardware-conditioned neural decoder framework designed to exploit the natural separation of timescales in superconducting processors.<n>We demonstrate that the FiLM-conditioned decoder achieves up to an 11.1x reduction in logical error rate relative to modified minimum-weight perfect matching.
• Score: 7.856983722811477
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Real-time decoding of quantum error correction (QEC) is essential for enabling fault-tolerant quantum computation. A practical decoder must operate with high accuracy at low latency, while remaining robust to spatial and temporal variations in hardware noise. We introduce a hardware-conditioned neural decoder framework designed to exploit the natural separation of timescales in superconducting processors, where calibration drifts occur over hours while error correction requires microsecond-scale responses. By processing calibration data through a graph-based encoder and conditioning a lightweight convolutional backbone via feature-wise linear modulation (FiLM), we decouple the heavy processing of device statistics from the low-latency syndrome decoding. We evaluate this approach using the 1D repetition code as a testbed on IBM Fez, Kingston, and Pittsburgh processors, collecting over 2.7 million experimental shots spanning distances up to d = 11. We demonstrate that a single trained model generalizes to unseen qubit chains and new calibration data acquired days later without retraining. On these unseen experiments, the FiLM-conditioned decoder achieves up to an 11.1x reduction in logical error rate relative to modified minimum-weight perfect matching. We observe that by employing a network architecture that exploits the highly asynchronous nature of system calibration and decoding, hardware-conditioned neural decoding demonstrates promising, adaptive performance with negligible latency overhead relative to unconditioned baselines.

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