Year: 2023
Type: article
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.
Institutions: Centrum Wiskunde & Informatica, QuSoft, University of Amsterdam, Freie Universität Berlin, Humboldt-Universität zu Berlin, University College London, Technical University of Munich, University of Copenhagen, Fraunhofer Institute for Telecommunications, Heinrich Hertz Institute, Helmholtz-Zentrum Berlin für Materialien und Energie, Technology Innovation Institute
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4286894942