Adaptive Estimation of Drifting Noise in Quantum Error Correction

• URL: http://arxiv.org/abs/2511.09491v1
• Date: Thu, 13 Nov 2025 01:57:58 GMT
• Title: Adaptive Estimation of Drifting Noise in Quantum Error Correction
• Authors: Devansh Bhardwaj, Evangelia Takou, Yingjia Lin, Kenneth R. Brown,
• Abstract summary: We present a framework to capture time-dependent Pauli noise, by exploiting the syndrome statistics of quantum error correction experiments.<n>We prove the noise-filtering behavior of sliding windows, linking window size to spectral cutoff frequencies, and provide an iterative algorithm that captures multiple drift frequencies.<n>Our window-based estimation methods and adaptive decoding offer new insights into noise spectroscopy and decoder optimization under drift.
• Score: 1.1998722332188005
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Advancing quantum information processors and building fault-tolerant architectures rely on the ability to accurately characterize the noise sources and suppress their impact on quantum devices. In practice, noise often drifts over time, whereas conventional noise characterization and decoding methods typically assume stationarity or provide only a time-average behavior of the noise. This treatment can result in suboptimal decoding performance. In this work, we present a rigorous analytical framework to capture time-dependent Pauli noise, by exploiting the syndrome statistics of quantum error correction experiments. We propose a sliding-window estimation method which allows us to recover the frequency components of the noise, by using optimal window sizes that we derive analytically. We prove the noise-filtering behavior of sliding windows, linking window size to spectral cutoff frequencies, and provide an iterative algorithm that captures multiple drift frequencies. We further introduce an overlapping window algorithm that enables us to capture rapid multi-frequency noise drifts in a single-pass fashion. Simulations for both phenomenological and circuit-level noise models validate our framework, demonstrating robust tracking of multi-frequency drift. The logical error rate obtained from our estimated models consistently align with the ground-truth logical error rate, and we find suppression of logical errors compared to static error models. Our window-based estimation methods and adaptive decoding offer new insights into noise spectroscopy and decoder optimization under drift using only syndrome data.

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