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Goal-Oriented Bayesian Optimal Experimental Design for Nonlinear Models using Markov Chain Monte Carlo
We present a computational framework of predictive goal-oriented OED (GO-OED) suitable for nonlinear observation and prediction models, which seeks the experimental design providing the greatest EIG on the QoIs.
Learning a quantum computer's capability using convolutional neural networks
We explore how to define the capability function, and we explain how data for training neural networks can be efficiently obtained for a capability function defined using process fidelity.
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