Gate Set Tomography

Erik Nielsen1, John King Gamble2, Kenneth Rudinger1, Travis Scholten3, Kevin Young1, and Robin Blume-Kohout1

1Quantum Performance Laboratory, Sandia National Laboratories
2Microsoft Research
3IBM Quantum, IBM T.J. Watson Research Center

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Abstract

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.

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The above citations are from Crossref's cited-by service (last updated successfully 2024-03-18 16:19:46) and SAO/NASA ADS (last updated successfully 2024-03-18 16:19:47). The list may be incomplete as not all publishers provide suitable and complete citation data.