Cross-Platform Autonomous Control of Minimal Kitaev Chains
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arXiv
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| Autores principales: | , , , , , , , , , , , , , , , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | |
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| author | van Driel, David Koch, Rouven Sietses, Vincent P. M. Haaf, Sebastiaan L. D. ten Liu, Chun-Xiao Zatelli, Francesco Roovers, Bart Bordin, Alberto van Loo, Nick Wang, Guanzhong Wolff, Jan Cornelis Mazur, Grzegorz P. Dvir, Tom Kulesh, Ivan Wang, Qingzhen Bozkurt, A. Mert Gazibegovic, Sasa Badawy, Ghada Bakkers, Erik P. A. M. Wimmer, Michael Goswami, Srijit Lado, Jose L. Kouwenhoven, Leo P. Greplova, Eliska |
| author_facet | van Driel, David Koch, Rouven Sietses, Vincent P. M. Haaf, Sebastiaan L. D. ten Liu, Chun-Xiao Zatelli, Francesco Roovers, Bart Bordin, Alberto van Loo, Nick Wang, Guanzhong Wolff, Jan Cornelis Mazur, Grzegorz P. Dvir, Tom Kulesh, Ivan Wang, Qingzhen Bozkurt, A. Mert Gazibegovic, Sasa Badawy, Ghada Bakkers, Erik P. A. M. Wimmer, Michael Goswami, Srijit Lado, Jose L. Kouwenhoven, Leo P. Greplova, Eliska |
| contents | Contemporary quantum devices are reaching new limits in size and complexity, allowing for the experimental exploration of emergent quantum modes. However, this increased complexity introduces significant challenges in device tuning and control. Here, we demonstrate autonomous tuning of emergent Majorana zero modes in a minimal realization of a Kitaev chain. We achieve this task using cross-platform transfer learning. First, we train a tuning model on a theory model. Next, we retrain it using a Kitaev chain realization in a two-dimensional electron gas. Finally, we apply this model to tune a Kitaev chain realized in quantum dots coupled through a semiconductor-superconductor section in a one-dimensional nanowire. Utilizing a convolutional neural network, we predict the tunneling and Cooper pair splitting rates from differential conductance measurements, employing these predictions to adjust the electrochemical potential to a Majorana sweet spot. The algorithm successfully converges to the immediate vicinity of a sweet spot (within 1.5 mV in 67.6% of attempts and within 4.5 mV in 80.9% of cases), typically finding a sweet spot in 45 minutes or less. This advancement is a stepping stone towards autonomous tuning of emergent modes in interacting systems, and towards foundational tuning machine learning models that can be deployed across a range of experimental platforms. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_04596 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Cross-Platform Autonomous Control of Minimal Kitaev Chains van Driel, David Koch, Rouven Sietses, Vincent P. M. Haaf, Sebastiaan L. D. ten Liu, Chun-Xiao Zatelli, Francesco Roovers, Bart Bordin, Alberto van Loo, Nick Wang, Guanzhong Wolff, Jan Cornelis Mazur, Grzegorz P. Dvir, Tom Kulesh, Ivan Wang, Qingzhen Bozkurt, A. Mert Gazibegovic, Sasa Badawy, Ghada Bakkers, Erik P. A. M. Wimmer, Michael Goswami, Srijit Lado, Jose L. Kouwenhoven, Leo P. Greplova, Eliska Mesoscale and Nanoscale Physics Disordered Systems and Neural Networks Contemporary quantum devices are reaching new limits in size and complexity, allowing for the experimental exploration of emergent quantum modes. However, this increased complexity introduces significant challenges in device tuning and control. Here, we demonstrate autonomous tuning of emergent Majorana zero modes in a minimal realization of a Kitaev chain. We achieve this task using cross-platform transfer learning. First, we train a tuning model on a theory model. Next, we retrain it using a Kitaev chain realization in a two-dimensional electron gas. Finally, we apply this model to tune a Kitaev chain realized in quantum dots coupled through a semiconductor-superconductor section in a one-dimensional nanowire. Utilizing a convolutional neural network, we predict the tunneling and Cooper pair splitting rates from differential conductance measurements, employing these predictions to adjust the electrochemical potential to a Majorana sweet spot. The algorithm successfully converges to the immediate vicinity of a sweet spot (within 1.5 mV in 67.6% of attempts and within 4.5 mV in 80.9% of cases), typically finding a sweet spot in 45 minutes or less. This advancement is a stepping stone towards autonomous tuning of emergent modes in interacting systems, and towards foundational tuning machine learning models that can be deployed across a range of experimental platforms. |
| title | Cross-Platform Autonomous Control of Minimal Kitaev Chains |
| topic | Mesoscale and Nanoscale Physics Disordered Systems and Neural Networks |
| url | https://arxiv.org/abs/2405.04596 |