A Machine Learning Approach for Automated Fine-Tuning of Semiconductor Spin Qubits
ORAL
Abstract
In the search for a technology best suited for quantum computation, spin qubits based on gate-defined quantum dots have demonstrated very favorable properties, one remaining challenge is their tuning into a suitable operating regime. Since this requires accurate tuning of the voltages applied to all electrostatic gates, this is a time-consuming procedure [1]. Thus, 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 benchmark its performance by tuning tunnel and lead coupling on a GaAs singlet triplet qubit. We employ a Bayesian approach called Kalman filter to estimate the gradients of the parameters of interest as function of gate voltages. Our benchmarks demonstrate the ability of reaching the operation regime within 3 to 5 iterations, corresponding to 10 to 15 minutes of lab-time.
1T. Botzem et al., “Tuning methods for semiconductor spin–qubits,” , 1–12 (2018), arXiv:1801.0375
We present an algorithm for the automated fine-tuning of quantum dots, and benchmark its performance by tuning tunnel and lead coupling on a GaAs singlet triplet qubit. We employ a Bayesian approach called Kalman filter to estimate the gradients of the parameters of interest as function of gate voltages. Our benchmarks demonstrate the ability of reaching the operation regime within 3 to 5 iterations, corresponding to 10 to 15 minutes of lab-time.
1T. Botzem et al., “Tuning methods for semiconductor spin–qubits,” , 1–12 (2018), arXiv:1801.0375
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Presenters
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Julian David Teske
- JARA-FIT Institute Quantum Information, Forschungszentrum Jülich GmbH and RWTH Aachen University, 52074 Aachen, Germany
- JARA-FIT Institute for Quantum Information, Forschungszentrum Jülich GmbH and RWTH Aachen University, 52074 Aachen, Germany