Robust characterization of average gate set noise and cross-talk using gate-set shadows

Abstract

As quantum processors scale, robust and scalable tools to characterize gate-level noise become indispensable. Randomized benchmarking (RB) and its many variants have emerged as the go-to protocols in practice: they yield a single number to characterize a gate set, the average gate fidelity, per irreducible subspace. It has been argued that RB can in principle be extended into a full tomographic framework (RB tomography), but large-scale practical implementations, even of partial tomographic information, have long been hindered by the need for sequence inversion, explicit gate interleaving, and the required measurement statistics. In this talk, we report on a scalable implementation of a flexible and efficient variant of RB tomography called gate-set shadows, on a 36-qubit superconducting processor. From a single, efficiently implemented randomized-circuit experiment, we reconstruct the one- and two-qubit marginals of the average noise channel, learn classically correlated quantum noise including cross-talk, and infer a variety of diagnostic information and performance metrics. We decompose the reconstructed channels into error-generator rates that separate coherent, stochastic, and correlated contributions, and find cross-talk that is structured but not confined to the chip's physical topology, escaping several standard pairwise cross-talk metrics. We further introduce a way to test how much of this reconstructed noise is explained by mechanisms already assumed in the literature, such as detuning, over-rotation, and decoherence: fitting the reconstructed rates against a library of such models and quantifying what is left over. First results suggest that cross-talk can resist explanation by standard noise models more than commonly assumed.