How much can we learn from quantum random circuit sampling?

Benchmarking quantum devices is central to developing quantum technologies, but it is challenging to benchmark large-scale quantum devices in full operations. We introduce new benchmarking methods based on random circuit sampling (RCS) that substantially extend the scope of conventional approaches. Our framework extracts rich diagnostic information, including spatiotemporal error profiles, correlated and contextual errors, and biased readout errors, without requiring any modifications of the experiment. We also develop techniques for benchmarking in the beyond-classical regime, by leveraging side information in the form of bitstring samples obtained from reference quantum devices. 

Our approach is based on advanced high-dimensional statistical modeling of RCS data: we establish the information-theoretic limits of error estimation, finding phase transitions in learnability as the amount of side information varies. We demonstrate our methods using publicly available RCS data from a state-of-the-art superconducting processor, obtaining in situ characterizations that are qualitatively consistent yet quantitatively distinct from component-level calibrations. Our results establish both practical benchmarking protocols for current and future quantum computers and fundamental information-theoretic limits on how much can be learned from RCS data.