Neural network based time-resolved state tomography of superconducting qubits
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866914715239710720 |
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| author | You, Ziyang Duan, Jiheng Huang, Wenhui Zhang, Libo Liu, Song Zhong, Youpeng Ian, Hou |
| author_facet | You, Ziyang Duan, Jiheng Huang, Wenhui Zhang, Libo Liu, Song Zhong, Youpeng Ian, Hou |
| contents | Superconducting qubits have emerged as a premier platform for large-scale quantum computation, yet the fidelity of state readout is often hindered by random noise and crosstalk, especially in multi-qubit systems. While neural networks trained on labeled data have shown promise in reducing crosstalk effects during readout, their current capabilities are limited to binary discrimination of joint-qubit states due to architectural constraints. Here we introduce a time-resolved modulated neural network capable of full-state tomography for individual qubits, enabling detailed time-resolved measurements like Rabi oscillations. This scalable approach, with a dedicated module per qubit, mitigated readout error by an order of magnitude under low signal-to-noise ratios and substantially reduced variance in Rabi oscillation measurements. This advancement bolsters quantum state discrimination with neural networks, and propels the development of next-generation quantum processors with enhanced performance and scalability. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2312_07958 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Neural network based time-resolved state tomography of superconducting qubits You, Ziyang Duan, Jiheng Huang, Wenhui Zhang, Libo Liu, Song Zhong, Youpeng Ian, Hou Quantum Physics Superconducting qubits have emerged as a premier platform for large-scale quantum computation, yet the fidelity of state readout is often hindered by random noise and crosstalk, especially in multi-qubit systems. While neural networks trained on labeled data have shown promise in reducing crosstalk effects during readout, their current capabilities are limited to binary discrimination of joint-qubit states due to architectural constraints. Here we introduce a time-resolved modulated neural network capable of full-state tomography for individual qubits, enabling detailed time-resolved measurements like Rabi oscillations. This scalable approach, with a dedicated module per qubit, mitigated readout error by an order of magnitude under low signal-to-noise ratios and substantially reduced variance in Rabi oscillation measurements. This advancement bolsters quantum state discrimination with neural networks, and propels the development of next-generation quantum processors with enhanced performance and scalability. |
| title | Neural network based time-resolved state tomography of superconducting qubits |
| topic | Quantum Physics |
| url | https://arxiv.org/abs/2312.07958 |