Neural-network autotuning combined with FPGA-accelerated RF reflectometry reduces stability-diagram acquisition time by 9.8x and total single-electron-regime initialization time by 2.2x in a SiGe quantum dot.
Ray-Based Framework for State Identification in Quantum Dot Devices,
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Rapid Autotuning of a SiGe Quantum Dot into the Single-Electron Regime with Machine Learning and RF-Reflectometry FPGA-Based Measurements
Neural-network autotuning combined with FPGA-accelerated RF reflectometry reduces stability-diagram acquisition time by 9.8x and total single-electron-regime initialization time by 2.2x in a SiGe quantum dot.