Neural Network-Based Frequency Optimization for Superconducting Quantum Chips

• URL: http://arxiv.org/abs/2412.01183v4
• Date: Fri, 20 Dec 2024 05:29:43 GMT
• Title: Neural Network-Based Frequency Optimization for Superconducting Quantum Chips
• Authors: Bin-Han Lu, Peng Wang, Qing-Song Li, Yu-Chun Wu, Zhao-Yun Chen, Guo-Ping Guo,
• Abstract summary: We propose a neural network-based frequency configuration approach for superconducting quantum chips. A trained neural network model estimates frequency configuration errors, and an intermediate optimization strategy identifies optimal configurations within localized regions of the chip. We also design a crosstalk-aware hardware-efficient ansatz for variational quantum eigensolvers, achieving improved energy computations.
• Score: 3.4802501242383146
• License:
• Abstract: 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.

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