Preparing Schrödinger Cat States in a Microwave Cavity Using a Neural Network
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-n...
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| Main Authors: | , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
American Physical Society
2025-01-01
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| Series: | PRX Quantum |
| Online Access: | http://doi.org/10.1103/PRXQuantum.6.010321 |
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| _version_ | 1832575983622815744 |
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| author | Hector Hutin Pavlo Bilous Chengzhi Ye Sepideh Abdollahi Loris Cros Tom Dvir Tirth Shah Yonatan Cohen Audrey Bienfait Florian Marquardt Benjamin Huard |
| author_facet | Hector Hutin Pavlo Bilous Chengzhi Ye Sepideh Abdollahi Loris Cros Tom Dvir Tirth Shah Yonatan Cohen Audrey Bienfait Florian Marquardt Benjamin Huard |
| author_sort | Hector Hutin |
| collection | DOAJ |
| description | 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 | Article |
| id | doaj-art-2e1f468172e143abb9696ef9c0ddf168 |
| institution | Kabale University |
| issn | 2691-3399 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | American Physical Society |
| record_format | Article |
| series | PRX Quantum |
| spelling | doaj-art-2e1f468172e143abb9696ef9c0ddf1682025-01-31T15:31:17ZengAmerican Physical SocietyPRX Quantum2691-33992025-01-016101032110.1103/PRXQuantum.6.010321Preparing Schrödinger Cat States in a Microwave Cavity Using a Neural NetworkHector HutinPavlo BilousChengzhi YeSepideh AbdollahiLoris CrosTom DvirTirth ShahYonatan CohenAudrey BienfaitFlorian MarquardtBenjamin HuardScaling 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.http://doi.org/10.1103/PRXQuantum.6.010321 |
| spellingShingle | Hector Hutin Pavlo Bilous Chengzhi Ye Sepideh Abdollahi Loris Cros Tom Dvir Tirth Shah Yonatan Cohen Audrey Bienfait Florian Marquardt Benjamin Huard Preparing Schrödinger Cat States in a Microwave Cavity Using a Neural Network PRX Quantum |
| title | Preparing Schrödinger Cat States in a Microwave Cavity Using a Neural Network |
| title_full | Preparing Schrödinger Cat States in a Microwave Cavity Using a Neural Network |
| title_fullStr | Preparing Schrödinger Cat States in a Microwave Cavity Using a Neural Network |
| title_full_unstemmed | Preparing Schrödinger Cat States in a Microwave Cavity Using a Neural Network |
| title_short | Preparing Schrödinger Cat States in a Microwave Cavity Using a Neural Network |
| title_sort | preparing schrodinger cat states in a microwave cavity using a neural network |
| url | http://doi.org/10.1103/PRXQuantum.6.010321 |
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