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Search Publications by: Sean Blakley (Assoc)

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Displaying 1 - 3 of 3

Simplified algorithms for adaptive experiment design in parameter estimation

November 1, 2022
Author(s)
Robert D. McMichael, Sean Blakley
In experiments to estimate parameters of a parametric model, Bayesian experiment design allows measurement settings to be chosen based on utility, i.e. the predicted improvement of parameter distributions due to modeled measurement results. In this paper

Sequential Bayesian experiment design for adaptive Ramsey sequence measurements.

October 11, 2021
Author(s)
Robert D. McMichael, Sergey Dushenko, Sean Blakley
The Ramsey sequence is a canonical example of a quantum phase determination for a spin qubit, but when readout fidelity is low, as with NV centers, measurement efficiency can be increased by focusing measurement resources on the most productive settings

optbayesexpt: Sequential Bayesian Experiment Design for Adaptive Measurements

February 3, 2021
Author(s)
Robert McMichael, Sean M. Blakley, Sergey Dushenko
Optbayesexpt is a free, open-source python package that provides adaptive algorithms for efficient estimation/measurement of parameters in a model function. Parameter estimation is the type of measurement one would conventionally tackle with a sequence of