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

As quantum processors advance, 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. But large scale practical implementations of even partial RB tomography have long been hindered by the need for sequence inversion, gate interleaving, and the required measurement statistics. In this talk, we report on scalable implementations of a flexible and efficient variant of RB tomography called gate-set shadows on super-conducting qubit computing platforms. From one efficiently implemented randomized circuit experiment, we reconstruct marginals of the average noise channel, learn classically correlated quantum noise, including cross-talk, and infer a variety of diagnostic information and performance metrics.