Refining qubit calibration models using supervised learning

Abstract

A computer-implemented method for refining a qubit calibration model is described. The method comprises receiving, at a learning module, training data, wherein the training data comprises a plurality of calibration data sets, wherein each calibration data set is derived from a system comprising one or more qubits, and a plurality of parameter sets, each parameter set comprising extracted parameters obtained using a corresponding calibration data set, wherein extracting the parameters includes fitting a qubit calibration model to the corresponding calibration data set using a fitter algorithm. The method further comprises executing, at the learning module, a supervised machine learning algorithm which processes the training data to learn a perturbation to the qubit calibration model that captures one or more features in the plurality of calibration data sets that are not captured by the qubit calibration model, thereby to provide a refined qubit calibration model.