Estimation of Temporally-Correlated Noise Processes in Quantum Computers

Non-markovian noise arising from temporally correlated fluctuations in device parameters is common in real systems and can corrupt estimates for the markovian noise in a device. In this work we demonstrate an extension of gate set tomography (GST) based on promoting parameters of a markovian noise model to stochastic processes. This work builds on top of a novel approach for the analytic forward simulation of sparse error generator models, as well as tools from quantum statistical mechanics such as the generalized cumulant expansion method. We demonstrate the use of these extended models in the estimation of non-Markovian noise parameters for a broad class of realistic temporal correlations. We additionally discuss the development of new modules for the software package pyGSTi which allow for the flexible and efficient description, simulation and estimation of device models in the presence of temporally correlated noise.

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