Automated Model Selection for Gate Set Tomography

Gate set tomography (GST) is a characterization technique that provides high accuracy estimates of a quantum processor’s operations. Traditional GST models each gate as a dense process matrix, which can be prohibitively expensive to fit for multiple qubits and difficult to interpret. Reduced models that only contain a subset of the model parameters can mitigate both issues, but model selection, i.e. choosing which parameters to keep, remains an open problem. To that end, we present algorithms for performing automated model selection, allowing the construction of reduced models without needing a priori knowledge of device physics. We demonstrate our method with both simulations and by comparing against hand-tuned reduced models from existing experiments. SNL is managed and operated by NTESS under DOE NNSA contract DE-NA0003525.