All rf-based tuning algorithm for quantum devices using machine learning
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arXiv
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| Main Authors: | , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2022
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| _version_ | 1866911356564799488 |
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| author | van Straaten, Barnaby Fedele, Federico Vigneau, Florian Hickie, Joseph Jirovec, Daniel Ballabio, Andrea Chrastina, Daniel Isella, Giovanni Katsaros, Georgios Ares, Natalia |
| author_facet | van Straaten, Barnaby Fedele, Federico Vigneau, Florian Hickie, Joseph Jirovec, Daniel Ballabio, Andrea Chrastina, Daniel Isella, Giovanni Katsaros, Georgios Ares, Natalia |
| contents | Radio-frequency measurements could satisfy DiVincenzo's readout criterion in future large-scale solid-state quantum processors, as they allow for high bandwidths and frequency multiplexing. However, the scalability potential of this readout technique can only be leveraged if quantum device tuning is performed using exclusively radio-frequency measurements i.e. without resorting to current measurements. We demonstrate an algorithm that automatically tunes double quantum dots using only radio-frequency reflectometry. Exploiting the high bandwidth of radio-frequency measurements, the tuning was completed within a few minutes without prior knowledge about the device architecture. Our results show that it is possible to eliminate the need for transport measurements for quantum dot tuning, paving the way for more scalable device architectures. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2211_04504 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | All rf-based tuning algorithm for quantum devices using machine learning van Straaten, Barnaby Fedele, Federico Vigneau, Florian Hickie, Joseph Jirovec, Daniel Ballabio, Andrea Chrastina, Daniel Isella, Giovanni Katsaros, Georgios Ares, Natalia Mesoscale and Nanoscale Physics Quantum Physics Radio-frequency measurements could satisfy DiVincenzo's readout criterion in future large-scale solid-state quantum processors, as they allow for high bandwidths and frequency multiplexing. However, the scalability potential of this readout technique can only be leveraged if quantum device tuning is performed using exclusively radio-frequency measurements i.e. without resorting to current measurements. We demonstrate an algorithm that automatically tunes double quantum dots using only radio-frequency reflectometry. Exploiting the high bandwidth of radio-frequency measurements, the tuning was completed within a few minutes without prior knowledge about the device architecture. Our results show that it is possible to eliminate the need for transport measurements for quantum dot tuning, paving the way for more scalable device architectures. |
| title | All rf-based tuning algorithm for quantum devices using machine learning |
| topic | Mesoscale and Nanoscale Physics Quantum Physics |
| url | https://arxiv.org/abs/2211.04504 |