Shadow estimation of gate-set properties from random sequences

Abstract With quantum computing devices increasing in scale and complexity, there is a growing need for tools that obtain precise diagnostic information about quantum operations. However, current quantum devices are only capable of short unstructured gate sequences followed by native measurements. W...

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Bibliographic Details
Published inNature communications Vol. 14; no. 1; p. 5039
Main Authors Helsen, J., Ioannou, M., Kitzinger, J., Onorati, E., Werner, A. H., Eisert, J., Roth, I.
Format Journal Article
LanguageEnglish
Published London Nature Publishing Group 19.08.2023
Nature Publishing Group UK
Nature Portfolio
Subjects
Complexity
Crosstalk
Estimation
Experiments
Optimization
Protocol
Quantum computing
Shadows
Tomography
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Summary:Abstract With quantum computing devices increasing in scale and complexity, there is a growing need for tools that obtain precise diagnostic information about quantum operations. However, current quantum devices are only capable of short unstructured gate sequences followed by native measurements. We accept this limitation and turn it into a new paradigm for characterizing quantum gate-sets. A single experiment—random sequence estimation—solves a wealth of estimation problems, with all complexity moved to classical post-processing. We derive robust channel variants of shadow estimation with close-to-optimal performance guarantees and use these as a primitive for partial, compressive and full process tomography as well as the learning of Pauli noise. We discuss applications to the quantum gate engineering cycle, and propose novel methods for the optimization of quantum gates and diagnosing cross-talk.
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ISSN:2041-1723
2041-1723
DOI:10.1038/s41467-023-39382-9