Singlet-triplet-state readout in silicon-metal-oxide-semiconductor double quantum dots
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arXiv
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| Main Authors: | , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866913467562196992 |
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| author | Ma, Rong-Long Zhu, Sheng-Kai Kong, Zhen-Zhen Sun, Tai-Ping Ni, Ming Zhou, Yu-Chen Zhou, Yuan Luo, Gang Cao, Gang Wang, Gui-Lei Li, Hai-Ou Guo, Guo-Ping |
| author_facet | Ma, Rong-Long Zhu, Sheng-Kai Kong, Zhen-Zhen Sun, Tai-Ping Ni, Ming Zhou, Yu-Chen Zhou, Yuan Luo, Gang Cao, Gang Wang, Gui-Lei Li, Hai-Ou Guo, Guo-Ping |
| contents | High-fidelity singlet-triplet state readout is essential for large-scale quantum computing. However, the widely used threshold method of comparing a mean value with the fixed threshold will limit the judgment accuracy, especially for the relaxed triplet state, under the restriction of relaxation time and signal-to-noise ratio. Here, we achieve an enhanced latching readout based on Pauli spin blockade in a Si-MOS double quantum dot device and demonstrate an average singlet-triplet state readout fidelity of 97.59% by the threshold method. We reveal the inherent deficiency of the threshold method for the relaxed triplet state classification and introduce machine learning as a relaxation-independent readout method to reduce the misjudgment. The readout fidelity for classifying the simulated single-shot traces can be improved to 99.67% by machine learning method, better than the threshold method of 97.54% which is consistent with the experimental result. This work indicates that machine learning method can be a strong potential candidate for alleviating the restrictions of stably achieving high-fidelity and high-accuracy singlet-triplet state readout in large-scale quantum computing. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2309_09723 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Singlet-triplet-state readout in silicon-metal-oxide-semiconductor double quantum dots Ma, Rong-Long Zhu, Sheng-Kai Kong, Zhen-Zhen Sun, Tai-Ping Ni, Ming Zhou, Yu-Chen Zhou, Yuan Luo, Gang Cao, Gang Wang, Gui-Lei Li, Hai-Ou Guo, Guo-Ping Mesoscale and Nanoscale Physics Quantum Physics High-fidelity singlet-triplet state readout is essential for large-scale quantum computing. However, the widely used threshold method of comparing a mean value with the fixed threshold will limit the judgment accuracy, especially for the relaxed triplet state, under the restriction of relaxation time and signal-to-noise ratio. Here, we achieve an enhanced latching readout based on Pauli spin blockade in a Si-MOS double quantum dot device and demonstrate an average singlet-triplet state readout fidelity of 97.59% by the threshold method. We reveal the inherent deficiency of the threshold method for the relaxed triplet state classification and introduce machine learning as a relaxation-independent readout method to reduce the misjudgment. The readout fidelity for classifying the simulated single-shot traces can be improved to 99.67% by machine learning method, better than the threshold method of 97.54% which is consistent with the experimental result. This work indicates that machine learning method can be a strong potential candidate for alleviating the restrictions of stably achieving high-fidelity and high-accuracy singlet-triplet state readout in large-scale quantum computing. |
| title | Singlet-triplet-state readout in silicon-metal-oxide-semiconductor double quantum dots |
| topic | Mesoscale and Nanoscale Physics Quantum Physics |
| url | https://arxiv.org/abs/2309.09723 |