Readout of strongly coupled NV center-pair spin states with deep neural networks
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866909441964638208 |
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| author | Joliffe, Matthew Vorobyov, Vadim Wrachtrup, Jörg |
| author_facet | Joliffe, Matthew Vorobyov, Vadim Wrachtrup, Jörg |
| contents | Optically addressable electron spin clusters are of interest for quantum computation, simulation and sensing. However, with interaction length scales of a few tens of nanometers in the strong coupling regime, they are unresolved in conventional confocal microscopy, making individual readout problematic. Here we show that when using a single shot readout technique, collective states of the combined register space become accessible. By using spin to charge conversion of the defects we draw the connection between the intricate photon count statistics with spin state tomography using deep neural networks. This approach is particularly versatile with further scaling the number of constituent spins in a cluster due to complexity of the analytical treatment. We perform a proof of concept measurement of the correlated classical signal, paving the way for using our technique in realistic applications. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2412_19581 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Readout of strongly coupled NV center-pair spin states with deep neural networks Joliffe, Matthew Vorobyov, Vadim Wrachtrup, Jörg Quantum Physics Data Analysis, Statistics and Probability Optically addressable electron spin clusters are of interest for quantum computation, simulation and sensing. However, with interaction length scales of a few tens of nanometers in the strong coupling regime, they are unresolved in conventional confocal microscopy, making individual readout problematic. Here we show that when using a single shot readout technique, collective states of the combined register space become accessible. By using spin to charge conversion of the defects we draw the connection between the intricate photon count statistics with spin state tomography using deep neural networks. This approach is particularly versatile with further scaling the number of constituent spins in a cluster due to complexity of the analytical treatment. We perform a proof of concept measurement of the correlated classical signal, paving the way for using our technique in realistic applications. |
| title | Readout of strongly coupled NV center-pair spin states with deep neural networks |
| topic | Quantum Physics Data Analysis, Statistics and Probability |
| url | https://arxiv.org/abs/2412.19581 |