تقرير
Automated tuning of double quantum dots into specific charge states using neural networks
| العنوان: | Automated tuning of double quantum dots into specific charge states using neural networks |
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| المؤلفون: | Durrer, Renato, Kratochwil, Benedikt, Koski, Jonne V., Landig, Andreas J., Reichl, Christian, Wegscheider, Werner, Ihn, Thomas, Greplova, Eliska |
| المصدر: | Phys. Rev. Applied 13, 054019 (2020) |
| سنة النشر: | 2019 |
| المجموعة: | Condensed Matter Quantum Physics |
| مصطلحات موضوعية: | Condensed Matter - Mesoscale and Nanoscale Physics, Quantum Physics |
| الوصف: | While quantum dots are at the forefront of quantum device technology, tuning multi-dot systems requires a lengthy experimental process as multiple parameters need to be accurately controlled. This process becomes increasingly time-consuming and difficult to perform manually as the devices become more complex and the number of tuning parameters grows. In this work, we present a crucial step towards automated tuning of quantum dot qubits. We introduce an algorithm driven by machine learning that uses a small number of coarse-grained measurements as its input and tunes the quantum dot system into a pre-selected charge state. We train and test our algorithm on a GaAs double quantum dot device and we consistently arrive at the desired state or its immediate neighborhood. Comment: 9 pages, 8 figures, code available at https://github.com/redur/auto-tuner |
| نوع الوثيقة: | Working Paper |
| DOI: | 10.1103/PhysRevApplied.13.054019 |
| URL الوصول: | http://arxiv.org/abs/1912.02777 |
| رقم الأكسشن: | edsarx.1912.02777 |
| قاعدة البيانات: | arXiv |
| DOI: | 10.1103/PhysRevApplied.13.054019 |
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