Kavli Affiliate: Irfan Siddiqi
| First 5 Authors: Elie Genois, Noah J. Stevenson, Noah Goss, Irfan Siddiqi, Alexandre Blais
| Summary:
Implementing fast and high-fidelity quantum operations using open-loop
quantum optimal control relies on having an accurate model of the quantum
dynamics. Any deviations between this model and the complete dynamics of the
device, such as the presence of spurious modes or pulse distortions, can
degrade the performance of optimal controls in practice. Here, we propose an
experimentally simple approach to realize optimal quantum controls tailored to
the device parameters and environment while specifically characterizing this
quantum system. Concretely, we use physics-inspired machine learning to infer
an accurate model of the dynamics from experimentally available data and then
optimize our experimental controls on this trained model. We show the power and
feasibility of this approach by optimizing arbitrary single-qubit operations on
a superconducting transmon qubit, using detailed numerical simulations. We
demonstrate that this framework produces an accurate description of the device
dynamics under arbitrary controls, together with the precise pulses achieving
arbitrary single-qubit gates with a high fidelity of about 99.99%.
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