This title appears in the Scientific Report :
2019
Please use the identifier:
http://dx.doi.org/10.1063/1.5088412 in citations.
Please use the identifier: http://hdl.handle.net/2128/22026 in citations.
A machine learning approach for automated fine-tuning of semiconductor spin qubits
A machine learning approach for automated fine-tuning of semiconductor spin qubits
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 procedur...
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Personal Name(s): | Teske, Julian |
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Humpohl, Simon / Otten, Rene / Bethke, Patrick / Cerfontaine, Pascal / Dedden, Jonas / Ludwig, Arne / Wieck, Andreas D. / Bluhm, Hendrik (Corresponding author) | |
Contributing Institute: |
JARA Institut Quanteninformation; PGI-11 |
Published in: | Applied physics letters, 114 (2019) 13, S. 133102 - |
Imprint: |
Melville, NY
American Inst. of Physics
2019
|
DOI: |
10.1063/1.5088412 |
Document Type: |
Journal Article |
Research Program: |
Controlling Collective States |
Link: |
Published on 2019-04-02. Available in OpenAccess from 2020-04-02. Published on 2019-04-02. Available in OpenAccess from 2020-04-02. |
Publikationsportal JuSER |
Please use the identifier: http://hdl.handle.net/2128/22026 in citations.
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. |