| Residential College | false |
| Status | 已發表Published |
| Machine-learning assisted qubit state tomography | |
IAN HOU
| |
| 2024-06 | |
| Size of Audience | 40 |
| Type of Speaker | Invited talk |
| Abstract | Machine learning, especially those using neural networks, has significantly improved many computational tasks such as image recognition. Recognizing the state of a superconducting qubit, in particular with higher degrees of signal-to-noise ratio and recognition rate over the conventional approach, is also an area that neural-network algorithms have helped on. There are already works that demonstrate this ability provided by machine learning but they focus on discrete discrimination of the two qubit states. In our work, we construct a time-resolved modulated neural network that detects the full tomography of arbitrary superposition states over steps extended in time and is scalable according to the number of qubits. We demonstrate the construction on an Xmon circuit to show its improved detection fidelity and reduction in detection variance. |
| Conference Date | 2024-06 |
| Conference Place | Taiwan-Hong Kong Joint Workshop on Quantum Science and Technology |
| Document Type | Presentation |
| Collection | INSTITUTE OF APPLIED PHYSICS AND MATERIALS ENGINEERING |
| Affiliation | University of Macau |
| First Author Affilication | University of Macau |
| Recommended Citation GB/T 7714 | IAN HOU. Machine-learning assisted qubit state tomography, 2024-06. |
| Files in This Item: | There are no files associated with this item. | |||||
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