Perturbative density matrix propagation in Gate Set Tomography

Model-based quantum tomography protocols like Gate Set Tomography optimize a noise model with some number of parameters in order to fit experimental data. As the number of qubits increases, two issues emerge: 1) the number of model parameters grows, and 2) the cost of propagating quantum states (density matrices) increases exponentially. The first issue can be addressed by considering reduced models that limit errors to being low-weight and geometrically local. In this talk, we focus on the second issue and present a method for performing approximate density matrix propagation based on perturbative expansions of error generators. The method is tailored to the likelihood optimization problem faced by model-based tomography protocols. We will discuss the advantages and drawbacks of using this method when characterizing the errors in up to 8-qubit systems.