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Data-efficient quantum noise modeling via machine learning

Yanjun Ji1,*, Marco Roth2,†, David A. Kreplin3, Ilia Polian4, and Frank K. Wilhelm1,5

  • *Contact author: y.ji@fz-juelich.de
  • †Contact author: marco.roth@ipa.fraunhofer.de
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Phys. Rev. Applied 25, 034051 – Published 16 March, 2026

DOI: https://doi.org/10.1103/5r9m-y6z6

Abstract

Maximizing the computational utility of near-term quantum processors requires predictive noise models that inform robust, noise-aware compilation and error mitigation. Conventional models often fail to capture the complex error dynamics of real hardware or require prohibitive characterization overhead. We introduce a data-efficient framework that first constructs a physically motivated, parameterized noise model, and subsequently employs machine-learning-driven Bayesian optimization to identify its parameters. Our approach circumvents costly characterization protocols by estimating algorithm- and hardware-specific error parameters directly from readily available experimental data derived from existing application and benchmark circuit executions. The generality and robustness of the framework are demonstrated across diverse algorithms and superconducting devices, yielding high-fidelity predictions by estimating an independent parameter set tailored to each specific algorithm-hardware context. Crucially, we show that a model calibrated exclusively on small-scale circuits accurately predicts the behavior of larger validation circuits. Our data-efficient approach achieves up to a 65% improvement in model fidelity quantified by the Hellinger distance between predicted and experimental circuit output distributions, compared to standard noise models derived from device properties. This work establishes a practical paradigm for application-aware noise characterization, enabling compilation and error-mitigation strategies tailored to the specific interplay between quantum algorithms and device-specific noise dynamics.

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Outline

Information

Phys. Rev. Applied 25, 034051– Published 16 March, 2026
  • Received 16 September 2025
  • Revised 2 January 2026
  • Accepted 10 February 2026
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