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arXiv:2506.10834

Automated All-RF Tuning for Spin Qubit Readout and Control

Cornelius Carlsson, Jaime Saez-Mollejo, Federico Fedele, Stefano Calcaterra, Daniel Chrastina, Giovanni Isella, Georgios Katsaros, Natalia Ares

Published Jun 12, 2025• 0 citations
arXivHTMLPDF

Abstract

Efficient tuning of spin qubits remains a major bottleneck in scaling semiconductor quantum dot-based quantum processors. A key challenge is the rapid identification of gate voltage regimes suitable for qubit initialisation, control, and readout. Here, we leverage radio-frequency charge sensing to automate spin qubit tuning, achieving a median tuning time of approximately 15 minutes. In a single continuous run, our routine identifies spin qubits at 12 distinct charge transitions in under 17 hours. Beyond tuning, our routine autonomously acquires data revealing the gate-voltage dependence of the exchange interaction, dephasing time, and quality factor -- quantities that vary substantially between charge configurations. These results represent a step change in high-throughput spin qubit tuning and provide a foundation for a systematic and automated exploration of semiconductor quantum circuits.

DOI
10.48550/arxiv.2506.10834

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Efficient tuning of spin qubits remains a major bottleneck in scaling semiconductor quantum dot-based quantum processors. A key challenge is the rapid identification of gate voltage regimes suitable for qubit initialisation, control, and readout. Here, we leverage radio-frequency charge sensing to automate spin qubit tuning, achieving a median tuning time of approximately 15 minutes. In a single continuous run, our routine identifies spin qubits at 12 distinct charge transitions in under 17 hours. Beyond tuning, our routine autonomously acquires data revealing the gate-voltage dependence of the exchange interaction, dephasing time, and quality factor -- quantities that vary substantially between charge configurations. These results represent a step change in high-throughput spin qubit tuning and provide a foundation for a systematic and automated exploration of semiconductor quantum circuits.
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While spin qubits based on gate-defined quantum dots have demonstrated very favorable properties for quantum computing, one remaining hurdle is the need to tune each of them into a good operating regime by adjusting the voltages applied to electrostatic gates. The automation of these tuning procedures is a necessary requirement for the operation of a quantum processor based on gate-defined quantum dots, which is yet to be fully addressed. We present an algorithm for the automated fine-tuning of quantum dots and demonstrate its performance on a semiconductor singlet-triplet qubit in GaAs. The algorithm employs a Kalman filter based on Bayesian statistics to estimate the gradients of the target parameters as a function of gate voltages, thus learning the system response. The algorithm's design is focused on the reduction of the number of required measurements. We experimentally demonstrate the ability to change the operation regime of the qubit within 3–5 iterations, corresponding to 10–15 min of lab-time.
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.
Recent progress on increasingly complex devices based on semiconductor spin qubits shows that automated methods are indispensable for efficient device characterization and further scale-up. The authors present fast, fully automated procedures to extract key device parameters, as well as various readout and initialization points for operating a two-electron qubit. These efficient, quantitative methods can be the basis for automatic tune-up, in which gate voltages are iteratively adjusted based on the deviation of measured parameters from targets. This would enable operation of devices with more and more qubits, and systematically optimize fabrication procedures.
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