A new microscopic model maps quantum dot device geometry directly to flopping-mode qubit parameters, reveals a tradeoff between fast electric driving and clean Rabi oscillations, and derives exchange coupling for capacitively coupled qubits.
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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.
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Microscopic modeling of flopping-mode quantum dot spin qubits
A new microscopic model maps quantum dot device geometry directly to flopping-mode qubit parameters, reveals a tradeoff between fast electric driving and clean Rabi oscillations, and derives exchange coupling for capacitively coupled qubits.
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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.