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| Main Authors: | , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2409.05557 |
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| _version_ | 1866912212297187328 |
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| author | Hutin, Hector Bilous, Pavlo Ye, Chengzhi Abdollahi, Sepideh Cros, Loris Dvir, Tom Shah, Tirth Cohen, Yonatan Bienfait, Audrey Marquardt, Florian Huard, Benjamin |
| author_facet | Hutin, Hector Bilous, Pavlo Ye, Chengzhi Abdollahi, Sepideh Cros, Loris Dvir, Tom Shah, Tirth Cohen, Yonatan Bienfait, Audrey Marquardt, Florian Huard, Benjamin |
| contents | Scaling up quantum computing devices requires solving ever more complex quantum control tasks. Machine learning has been proposed as a promising approach to tackle the resulting challenges. However, experimental implementations are still scarce. In this work, we demonstrate experimentally a neural-network-based preparation of Schrödinger cat states in a cavity coupled dispersively to a qubit. We show that it is possible to teach a neural network to output optimized control pulses for a whole family of quantum states. After being trained in simulations, the network takes a description of the target quantum state as input and rapidly produces the pulse shape for the experiment, without any need for time-consuming additional optimization or retraining for different states. Our experimental results demonstrate more generally how deep neural networks and transfer learning can produce efficient simultaneous solutions to a range of quantum control tasks, which will benefit not only state preparation but also parametrized quantum gates. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_05557 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Preparing Schrödinger cat states in a microwave cavity using a neural network Hutin, Hector Bilous, Pavlo Ye, Chengzhi Abdollahi, Sepideh Cros, Loris Dvir, Tom Shah, Tirth Cohen, Yonatan Bienfait, Audrey Marquardt, Florian Huard, Benjamin Quantum Physics Scaling up quantum computing devices requires solving ever more complex quantum control tasks. Machine learning has been proposed as a promising approach to tackle the resulting challenges. However, experimental implementations are still scarce. In this work, we demonstrate experimentally a neural-network-based preparation of Schrödinger cat states in a cavity coupled dispersively to a qubit. We show that it is possible to teach a neural network to output optimized control pulses for a whole family of quantum states. After being trained in simulations, the network takes a description of the target quantum state as input and rapidly produces the pulse shape for the experiment, without any need for time-consuming additional optimization or retraining for different states. Our experimental results demonstrate more generally how deep neural networks and transfer learning can produce efficient simultaneous solutions to a range of quantum control tasks, which will benefit not only state preparation but also parametrized quantum gates. |
| title | Preparing Schrödinger cat states in a microwave cavity using a neural network |
| topic | Quantum Physics |
| url | https://arxiv.org/abs/2409.05557 |