Scalable linearized gate set tomography
Fuente:
arXiv
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| Autori principali: | , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| Soggetti: | |
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| _version_ | 1866914555590868992 |
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| author | Miller, Ashe Ostrove, Corey Hines, Jordan Siekierski, Noah Young, Kevin Blume-Kohout, Robin Proctor, Timothy |
| author_facet | Miller, Ashe Ostrove, Corey Hines, Jordan Siekierski, Noah Young, Kevin Blume-Kohout, Robin Proctor, Timothy |
| contents | Characterizing errors on many-qubit quantum computers remains a key challenge to understanding and improving the performance of these devices. Current characterization methods either don't scale beyond a few qubits, or make simplifying assumptions (such as assuming stochastic Pauli error) that obscure the underlying physical error mechanisms. In this work, we present a scalable extension to gate set tomography-linearized gate set tomography-that enables characterization of many-qubit systems. Linearized gate set tomography relies on sparse error models, a linear approximation to enable efficient data fitting, and data from shallow circuits-so that the systematic error in the linear approximation is small. We demonstrate the accuracy of our technique using simulations of a ten-qubit system with coherent and stochastic errors, including coherent crosstalk, and we demonstrate that it is robust in presence of additional errors that are not included within the sparse error model ansatz. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_11158 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Scalable linearized gate set tomography Miller, Ashe Ostrove, Corey Hines, Jordan Siekierski, Noah Young, Kevin Blume-Kohout, Robin Proctor, Timothy Quantum Physics Characterizing errors on many-qubit quantum computers remains a key challenge to understanding and improving the performance of these devices. Current characterization methods either don't scale beyond a few qubits, or make simplifying assumptions (such as assuming stochastic Pauli error) that obscure the underlying physical error mechanisms. In this work, we present a scalable extension to gate set tomography-linearized gate set tomography-that enables characterization of many-qubit systems. Linearized gate set tomography relies on sparse error models, a linear approximation to enable efficient data fitting, and data from shallow circuits-so that the systematic error in the linear approximation is small. We demonstrate the accuracy of our technique using simulations of a ten-qubit system with coherent and stochastic errors, including coherent crosstalk, and we demonstrate that it is robust in presence of additional errors that are not included within the sparse error model ansatz. |
| title | Scalable linearized gate set tomography |
| topic | Quantum Physics |
| url | https://arxiv.org/abs/2605.11158 |