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Predictive Models from Quantum Computer Benchmarks
In the first case study, we predict circuit (i) process fidelities and (ii) success probabilities by fitting error rates models to two kinds of volumetric benchmarking data.
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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