Neural Network-Based Frequency Optimization for Superconducting Quantum Chips
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arXiv
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| Hauptverfasser: | , , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866915072410910720 |
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| author | Lu, Bin-Han Wang, Peng Li, Qing-Song Wu, Yu-Chun Chen, Zhao-Yun Guo, Guo-Ping |
| author_facet | Lu, Bin-Han Wang, Peng Li, Qing-Song Wu, Yu-Chun Chen, Zhao-Yun Guo, Guo-Ping |
| contents | Optimizing the frequency configuration of qubits and quantum gates in superconducting quantum chips presents a complex NP-complete optimization challenge. This process is critical for enabling practical control while minimizing decoherence and suppressing significant crosstalk. In this paper, we propose a neural network-based frequency configuration approach. A trained neural network model estimates frequency configuration errors, and an intermediate optimization strategy identifies optimal configurations within localized regions of the chip. The effectiveness of our method is validated through randomized benchmarking and cross-entropy benchmarking. Furthermore, we design a crosstalk-aware hardware-efficient ansatz for variational quantum eigensolvers, achieving improved energy computations. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_01183 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Neural Network-Based Frequency Optimization for Superconducting Quantum Chips Lu, Bin-Han Wang, Peng Li, Qing-Song Wu, Yu-Chun Chen, Zhao-Yun Guo, Guo-Ping Quantum Physics Optimizing the frequency configuration of qubits and quantum gates in superconducting quantum chips presents a complex NP-complete optimization challenge. This process is critical for enabling practical control while minimizing decoherence and suppressing significant crosstalk. In this paper, we propose a neural network-based frequency configuration approach. A trained neural network model estimates frequency configuration errors, and an intermediate optimization strategy identifies optimal configurations within localized regions of the chip. The effectiveness of our method is validated through randomized benchmarking and cross-entropy benchmarking. Furthermore, we design a crosstalk-aware hardware-efficient ansatz for variational quantum eigensolvers, achieving improved energy computations. |
| title | Neural Network-Based Frequency Optimization for Superconducting Quantum Chips |
| topic | Quantum Physics |
| url | https://arxiv.org/abs/2412.01183 |