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Disambiguating Pauli Noise in Quantum Computers

Edward H. Chen1,*, Senrui Chen2,†,‡, Laurin E. Fischer3,4,§, Andrew Eddins5, Luke C. G. Govia5, Brad Mitchell5, Andre He6, Youngseok Kim6, Liang Jiang2,∥ et al.

Alireza Seif6,¶

  • *Contact author: ehchen@alum.mit.edu
  • †Contact author: csenrui@gmail.com
  • ‡Present address: California Institute of Technology, Pasadena, CA, USA.
  • §Contact author: aur@zurich.ibm.com
  • ∥Contact author: liang.jiang@uchicago.edu
  • Contact author: alireza.seif@ibm.com
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PRX Quantum 7, 033045 – Published 3 September, 2026

DOI: https://doi.org/10.1103/69wc-gzl6

Abstract

To successfully perform quantum computations, it is often necessary to first accurately characterize the noise in the underlying hardware. However, it is well known that fundamental limitations prevent the unique identification of the noise model. This raises the question of whether these limitations impact the ability to predict noisy dynamics and mitigate errors. Here, we show, both theoretically and experimentally, that when learnable parameters are self-consistently characterized, the unlearnable (gauge) degrees of freedom do not impact predictions of noisy dynamics or error mitigation. We use the recently introduced framework of gate set Pauli noise learning to efficiently and self-consistently characterize and mitigate noise of a complete gate set, including state preparation, measurements, single-qubit gates and multi-qubit entangling Clifford gates. We validate our approach through experiments with up to 92 qubits and show that, while the gauge choice does not affect error-mitigated observables, optimizing it reduces sampling overhead.

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Popular Summary

Noise is one of the main obstacles to making quantum computers useful, so researchers need reliable ways to measure it and reduce its effects. A long-standing problem is that some parts of a quantum noise model cannot be uniquely determined from experiments, even in principle, raising concerns about whether such models can be trusted for predicting noisy behavior or mitigating errors. In this work, we show that this ambiguity does not have to be a problem, as long as the noise in state preparation, measurements, and gates is learned together in a self-consistent way. We develop and test a scalable method for doing this with Pauli noise models and demonstrate it on IBM quantum processors with experiments involving up to 92 qubits. The results show that self-consistent learning removes a previously overlooked source of bias in error mitigation. We also show that the remaining ambiguity can be optimized to reduce the number of samples needed, making error mitigation more practical for larger quantum computations.

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PRX Quantum 7, 033045– Published 3 September, 2026
  • Received 26 June 2025
  • Revised 24 March 2026
  • Accepted 9 July 2026
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