FACES is a new protocol for simultaneous self-consistent learning of averaged error rates across many FLO gates with rigorously shown efficient sampling complexity via Kravchuk transformations.
Robust, self-consistent, closed-form tomography of quantum logic gates on a trapped ion qubit
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abstract
We introduce and demonstrate experimentally: (1) a framework called "gate set tomography" (GST) for self-consistently characterizing an entire set of quantum logic gates on a black-box quantum device; (2) an explicit closed-form protocol for linear-inversion gate set tomography (LGST), whose reliability is independent of pathologies such as local maxima of the likelihood; and (3) a simple protocol for objectively scoring the accuracy of a tomographic estimate without reference to target gates, based on how well it predicts a set of testing experiments. We use gate set tomography to characterize a set of Clifford-generating gates on a single trapped-ion qubit, and compare the performance of (i) standard process tomography; (ii) linear gate set tomography; and (iii) maximum likelihood gate set tomography.
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GHZ states in X, Y, and Z bases form a maximally sensitive set allowing straightforward tests to identify coherent errors in quantum gates, measurements, and state preparation.
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Fermionic Averaged Circuit Eigenvalue Sampling
FACES is a new protocol for simultaneous self-consistent learning of averaged error rates across many FLO gates with rigorously shown efficient sampling complexity via Kravchuk transformations.
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Maximally Sensitive Sets of States
GHZ states in X, Y, and Z bases form a maximally sensitive set allowing straightforward tests to identify coherent errors in quantum gates, measurements, and state preparation.