Quantum Characterization
Beyond qubit readout, I have also worked on the detailed characterization of larger quantum systems, such as small quantum computing processors built from multiple qubits, with a common theme of improving the practical efficiency of characterization so that it is easier to deploy in the real world.
Improved efficiency can come at the cost of accuracy, which is the case for the Pairwise Perturbative Ansatz I developed to bootstrap process tomography on multi-qubit systems [2]. Nevertheless, such hardware/physics inspired assumptions are necessary to retrieve even partial information from larger systems. Without such information, calibration and debugging of quantum processors will be impossible at scale.
Even when mathematically efficient, the data collection that underlies quantum characterization can monopolize the limited time a quantum processor is up and running. My collaborators and I have leveraged results from applied mathematics, in particular, the Padua points for two-dimensional interpolation to accelerate both continuous variable state tomography [3], and calibration of multi-qubit arrays using spectator qubits [1].
Selected Papers
R. S. Gupta, L. C. G. Govia, and M. J. Biercuk,“Integration of spectator qubits into quantum computer architectures for hardware tuneup and calibration”, Phys. Rev. A 102, 042611 (2020).
L. C. G. Govia, G. J. Ribeill, D. Ristè, M. Ware, and H. Krovi,“Bootstrapping quantum process tomography via a perturbative ansatz”, Nat. Commun. 11, 1084 (2020).
O. Landon-Cardinal, L. C. G. Govia, and A. A. Clerk, “Quantitative tomography for continuous variable quantum systems”, Phys. Rev. Lett. 120 (9), 090501 (2018).