Rapid Autotuning of a SiGe Quantum Dot into the Single-Electron Regime with Machine Learning and RF-Reflectometry FPGA-Based Measurements

• URL: http://arxiv.org/abs/2509.19537v1
• Date: Tue, 23 Sep 2025 20:03:47 GMT
• Title: Rapid Autotuning of a SiGe Quantum Dot into the Single-Electron Regime with Machine Learning and RF-Reflectometry FPGA-Based Measurements
• Authors: Marc-Antoine Roux, Joffrey Rivard, Victor Yon, Alexis Morel, Dominic Leclerc, Claude Rohrbacher, El Bachir Ndiaye, Felice Francesco Tafuri, Brendan Bono, Stefan Kubicek, Roger Loo, Yosuke Shimura, Julien Jussot, Clément Godfrin, Danny Wan, Kristiaan De Greve, Marc-André Tétrault, Dominique Drouin, Christian Lupien, Michel Pioro-Ladrière, Eva Dupont-Ferrier,
• Abstract summary: We show a significant speedup by combining measurement speedup and a reduction in the number of measurements needed to tune a quantum dot device.<n>Using an autotuning algorithm based on a neural network and faster measurements by harnessing the FPGA embedded in Keysight's Quantum Engineering Toolkit (QET), the measurement time of stability diagrams has been reduced by a factor of 9.8.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Spin qubits need to operate within a very precise voltage space around charge state transitions to achieve high-fidelity gates. However, the stability diagrams that allow the identification of the desired charge states are long to acquire. Moreover, the voltage space to search for the desired charge state increases quickly with the number of qubits. Therefore, faster stability diagram acquisitions are needed to scale up a spin qubit quantum processor. Currently, most methods focus on more efficient data sampling. Our approach shows a significant speedup by combining measurement speedup and a reduction in the number of measurements needed to tune a quantum dot device. Using an autotuning algorithm based on a neural network and faster measurements by harnessing the FPGA embedded in Keysight's Quantum Engineering Toolkit (QET), the measurement time of stability diagrams has been reduced by a factor of 9.8. This led to an acceleration factor of 2.2 for the total initialization time of a SiGe quantum dot into the single-electron regime, which is limited by the Python code execution.

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