Disambiguating Pauli Noise in Quantum Computers

Year: 2026

Type: article

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 (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.

Source: PRX Quantum

DOI: 10.1103/69wc-gzl6

Authors: Edward H. Chen, Senrui Chen, Laurin E. Fischer, Andrew Eddins, Luke C. G. Govia, Brad Mitchell, Andre He, Youngseok Kim, Liang Jiang, Alireza Seif

Institutions: IBM (United States), University of Chicago, École Polytechnique Fédérale de Lausanne

Language: English

Topic: Quantum Computing Algorithms and Architecture

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $3,450

Funders: National Science Foundation, David and Lucile Packard Foundation, University of Chicago, European Commission, Multidisciplinary University Research Initiative, Air Force Office of Scientific Research, Army Research Office, Army Research Laboratory

OpenAlex ID: W7167832786