Gate Set Tomography
Gate set tomography (GST) is a protocol for detailed, predictive characterization of logic operations (gates) on quantum computing processors. Early versions of GST emerged around 2012-13, and since then it has been refined, demonstrated, and used in a large number of experiments. This paper present...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Verein zur Förderung des Open Access Publizierens in den Quantenwissenschaften
2021-10-01
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| Series: | Quantum |
| Online Access: | https://quantum-journal.org/papers/q-2021-10-05-557/pdf/ |
| _version_ | 1830339024787603456 |
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| author | Erik Nielsen John King Gamble Kenneth Rudinger Travis Scholten Kevin Young Robin Blume-Kohout |
| author_facet | Erik Nielsen John King Gamble Kenneth Rudinger Travis Scholten Kevin Young Robin Blume-Kohout |
| author_sort | Erik Nielsen |
| collection | DOAJ |
| description | Gate set tomography (GST) is a protocol for detailed, predictive characterization of logic operations (gates) on quantum computing processors. Early versions of GST emerged around 2012-13, and since then it has been refined, demonstrated, and used in a large number of experiments. This paper presents the foundations of GST in comprehensive detail. The most important feature of GST, compared to older state and process tomography protocols, is that it is $\textit{calibration-free}$. GST does not rely on pre-calibrated state preparations and measurements. Instead, it characterizes all the operations in a $\textit{gate set}$ simultaneously and self-consistently, relative to each other. Long sequence GST can estimate gates with very high precision and efficiency, achieving Heisenberg scaling in regimes of practical interest. In this paper, we cover GST's intellectual history, the techniques and experiments used to achieve its intended purpose, data analysis, gauge freedom and fixing, error bars, and the interpretation of gauge-fixed estimates of gate sets. Our focus is fundamental mathematical aspects of GST, rather than implementation details, but we touch on some of the foundational algorithmic tricks used in the $\texttt{pyGSTi}$ implementation. |
| first_indexed | 2024-12-19T20:49:53Z |
| format | Article |
| id | doaj.art-2ae9defcf5854abbbc867d071a416c82 |
| institution | Directory Open Access Journal |
| issn | 2521-327X |
| language | English |
| last_indexed | 2024-12-19T20:49:53Z |
| publishDate | 2021-10-01 |
| publisher | Verein zur Förderung des Open Access Publizierens in den Quantenwissenschaften |
| record_format | Article |
| series | Quantum |
| spelling | doaj.art-2ae9defcf5854abbbc867d071a416c822022-12-21T20:06:07ZengVerein zur Förderung des Open Access Publizierens in den QuantenwissenschaftenQuantum2521-327X2021-10-01555710.22331/q-2021-10-05-55710.22331/q-2021-10-05-557Gate Set TomographyErik NielsenJohn King GambleKenneth RudingerTravis ScholtenKevin YoungRobin Blume-KohoutGate set tomography (GST) is a protocol for detailed, predictive characterization of logic operations (gates) on quantum computing processors. Early versions of GST emerged around 2012-13, and since then it has been refined, demonstrated, and used in a large number of experiments. This paper presents the foundations of GST in comprehensive detail. The most important feature of GST, compared to older state and process tomography protocols, is that it is $\textit{calibration-free}$. GST does not rely on pre-calibrated state preparations and measurements. Instead, it characterizes all the operations in a $\textit{gate set}$ simultaneously and self-consistently, relative to each other. Long sequence GST can estimate gates with very high precision and efficiency, achieving Heisenberg scaling in regimes of practical interest. In this paper, we cover GST's intellectual history, the techniques and experiments used to achieve its intended purpose, data analysis, gauge freedom and fixing, error bars, and the interpretation of gauge-fixed estimates of gate sets. Our focus is fundamental mathematical aspects of GST, rather than implementation details, but we touch on some of the foundational algorithmic tricks used in the $\texttt{pyGSTi}$ implementation.https://quantum-journal.org/papers/q-2021-10-05-557/pdf/ |
| spellingShingle | Erik Nielsen John King Gamble Kenneth Rudinger Travis Scholten Kevin Young Robin Blume-Kohout Gate Set Tomography Quantum |
| title | Gate Set Tomography |
| title_full | Gate Set Tomography |
| title_fullStr | Gate Set Tomography |
| title_full_unstemmed | Gate Set Tomography |
| title_short | Gate Set Tomography |
| title_sort | gate set tomography |
| url | https://quantum-journal.org/papers/q-2021-10-05-557/pdf/ |
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