Efficient gate set tomography using compressed sensing

Gate set tomography (GST) is a widely used technique for characterizing a set of noisy quantum gates. GST is accurate and robust, but it is expensive: conventional GST requires running many circuits, and it uses intensive classical computation to analyze the data. GST fits an error model to data, and the model’s parameters correspond to the rates of different kinds of errors. Some of these parameters model commonly observed physical effects (e.g., gate over- or under-rotation errors, depolarization, amplitude damping), but many of the parameters model esoteric effects that are typically not present in real-world systems (e.g., high-weight Hamiltonian errors). We therefore expect many, or even most, of the true parameter values to be negligible or zero. When this is true, GST is estimating a sparse vector of parameters, and such vectors can be estimated with high efficiency using compressed sensing methods. In this talk, I explore the possibility of applying compressed sensing methods to GST. Our aim is to create a super-efficient version of GST that uses fast analysis on data from a practical number of circuits, and which is practical well beyond the 1-3 qubit regime in which conventional GST is feasible.

SNL is managed and operated by NTESS under DOE NNSA contract DE-NA0003525.