Journal
NEW JOURNAL OF PHYSICS
Volume 22, Issue 9, Pages -Publisher
IOP Publishing Ltd
DOI: 10.1088/1367-2630/abb64c
Keywords
quantum devices; machine learning; automatic tuning; variational auto-encoder
Categories
Funding
- Royal Society
- EPSRC National Quantum Technology Hub in Networked Quantum Information Technology [EP/M013243/1]
- EPSRC Platform Grant [EP/R029229/1]
- Quantum Technology Capital Grant [EP/N014995/1]
- Nokia
- Swiss NSF [179024]
- Swiss Nanoscience Institute
- EU H2020 European Microkelvin Platform EMP Grant [824109]
- Templeton World Charity Foundation
- John Templeton Foundation
- Lockheed Martin
- EPSRC [EP/R029229/1] Funding Source: UKRI
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Quantum devices with a large number of gate electrodes allow for precise control of device parameters. This capability is hard to fully exploit due to the complex dependence of these parameters on applied gate voltages. We experimentally demonstrate an algorithm capable of fine-tuning several device parameters at once. The algorithm acquires a measurement and assigns it a score using a variational auto-encoder. Gate voltage settings are set to optimize this score in real-time in an unsupervised fashion. We report fine-tuning times of a double quantum dot device within approximately 40 min.
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