How OpenSuperQ is planning to fully calibrate and characterize a 100 qubit superconducting QPU over the weekend

The current methodology of designing control pulses for superconducting circuits often results in an absurd situation: simplified analytic models which do not predict gate fidelities to high accuracy, and calibrated pulse shapes which achieve good fidelities, but do not correspond to the model. For large number of qubits the QPU calibration is long and difficult, and determining a detailed error budget is nearly impossible.

To rectify the situation, we have implemented a novel procedure of Combined Calibration and Characterization (C3): An interative combination of open-loop pulse optimization, model-free tune-up and iterative model fitting and refinement, utilizing a high-performance TensorFlow simulator. It allows for a rapid, and largely automated bring-up process of QPUs. The result is a high-fidelity model, comensurate high-fidelity gates and a detailed error budget.

The above components are then utilized to implement machine-learning capabilities such as adversarial system characterization and automated experiment design, to further accelerate the process of gaining insight into the behaviour of our systems.

C3 software will be made available as an open-source project.