Two feedforward DNNs map target cavity EM observables and qubit–cavity parameters (g, νq, α) to candidate SRF and transmon geometries that re-simulate to within ~5% and ~2%.
Padamsee,Superconducting radiofrequency technology for accelerators: state of the art and emerging trends (Wiley-VCH, USA, 2023)
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Neural-Network Inverse Design of SRF Cavities and Transmons for Bosonic Quantum Computation
Two feedforward DNNs map target cavity EM observables and qubit–cavity parameters (g, νq, α) to candidate SRF and transmon geometries that re-simulate to within ~5% and ~2%.