Shadow estimation of gate-set properties from random sequences

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. In order to be practical, schemes for characterizing quantum operations should require the simplest possible gate sequences and measurements. Here, the authors show how random gate sequences and native measurements (followed by classical post-processing) are sufficient for estimating several gate set properties.

Automated summary

With quantum computing devices becoming more complex, there is a need for tools that can provide precise diagnostic information about quantum operations. The authors propose a new approach that uses random gate sequences and native measurements followed by classical post-processing to estimate various gate set properties. They also discuss applications for optimizing quantum gates and diagnosing cross-talk. This research is important for the development and improvement of quantum computing devices.