Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Log inSign up
Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Blog
Job Board
Q3AS

© 2026 Aqora Quantum S.A.S.

TermsPrivacyLegal Notice

Precise, detailed error budgets for 2-qubit gates from gate set tomography.

Robin Blume-Kohout, Kenneth Rudinger, Erik Nielsen, Kevin C Young, Timothy J Proctor

Published Feb 1, 2022• 0 citations
Publisher
DOI
10.2172/2001895

Related papers

Gate Set Tomography on two qubits

Erik Nielsen, Robin Blume-Kohout, John King Gamble +1 more

Bulletin of the American Physical Society•Mar 14, 2016•0 citations

Gate Set Tomography of a Logical Qubit.

Kenneth Rudinger, Julie Campos, Mario Morford-Oberst +4 more

•Feb 1, 2022•0 citations

Gate set tomography on more than two qubits

Erik Nielsen, Robin Blume-Kohout, Kevin C Young +3 more

Bulletin of the American Physical Society•Mar 7, 2018•0 citations

Gate Set Tomography on a trapped ion qubit

Erik Nielsen, Robin Blume-Kohout, John King Gamble +4 more

Bulletin of the American Physical Society•Mar 5, 2015•0 citations

Gate-set tomography: calibration-free full characterization of quantum devices using error-amplifying circuits.

Robin Blume-Kohout

•Sep 1, 2014•0 citations

Logical gate set tomography

Piper C. Wysocki, Juan Gonzalez De Mendoza, Karl Mayer +11 more

Quantum error correction (QEC) can strongly suppress noise, but predicting whether a computation succeeds requires understanding not only how much effective noise persists after QEC, but what form that noise takes. To thoroughly characterize this noise, we introduce logical gate set tomography (``logical GST''). Logical GST produces detailed representations of noisy logical operations that can be used to derive higher-level metrics, such as gate fidelity and diamond distance, and to test for non-Markovian and coherent logical errors. We use logical GST to obtain the first full, self-consistent characterization of a universal single-qubit logical gate set, implemented in the seven-qubit Steane code using Quantinuum System Model H1, a 20-qubit trapped-ion quantum processor. In the experimental data from H1, logical GST identified predominantly logical Pauli-stochastic errors, found no evidence of logical non-Markovianity, and detected weak potential signatures of coherent logical error. By contrast, higher-shot datasets generated with Quantinuum's detailed system emulator, identify mechanisms for weak but statistically significant logical non-Markovianity and strong evidence of coherent logical error. Finally, we use logical GST to perform error attribution with the system emulator, revealing how specific physical error sources shape logical error mechanisms.

quant-pharXiv:2610.19034•Oct 15, 2026•0 citations
Quantum error correction (QEC) can strongly suppress noise, but predicting whether a computation succeeds requires understanding not only how much effective noise persists after QEC, but what form that noise takes. To thoroughly characterize this noise, we introduce logical gate set tomography (``logical GST''). Logical GST produces detailed representations of noisy logical operations that can be used to derive higher-level metrics, such as gate fidelity and diamond distance, and to test for non-Markovian and coherent logical errors. We use logical GST to obtain the first full, self-consistent characterization of a universal single-qubit logical gate set, implemented in the seven-qubit Steane code using Quantinuum System Model H1, a 20-qubit trapped-ion quantum processor. In the experimental data from H1, logical GST identified predominantly logical Pauli-stochastic errors, found no evidence of logical non-Markovianity, and detected weak potential signatures of coherent logical error. By contrast, higher-shot datasets generated with Quantinuum's detailed system emulator, identify mechanisms for weak but statistically significant logical non-Markovianity and strong evidence of coherent logical error. Finally, we use logical GST to perform error attribution with the system emulator, revealing how specific physical error sources shape logical error mechanisms.