Efficient flexible characterization of quantum processors with nested error models
arXiv:2103.02188 · doi:10.1088/1367-2630/ac20b9
Abstract
We present a simple and powerful technique for finding a good error model for a quantum processor. The technique iteratively tests a nested sequence of models against data obtained from the processor, and keeps track of the best-fit model and its wildcard error (a quantification of the unmodeled error) at each step. Each best-fit model, along with a quantification of its unmodeled error, constitute a characterization of the processor. We explain how quantum processor models can be compared with experimental data and to each other. We demonstrate the technique by using it to characterize a simulated noisy 2-qubit processor.
arXiv comment10 pages, 4 figures
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- Precision tomography of a three-qubit donor quantum processor in silicon
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- Benchmarking quantum logic operations relative to thresholds for fault tolerance
- Characterizing and mitigating coherent errors in a trapped ion quantum processor using hidden inverses