Neural Network-Based Frequency Optimization for Superconducting Quantum Chips  

作　　者：Bin-Han Lu Qing-Song Li Peng Wang Zhao-Yun Chen Yu-Chun Wu Guo-Ping Guo 

机构地区：[1]Key Laboratory of Quantum Information Chinese Academy of Sciences,School of Physics,University of Science and Technology of China,Hefei 230026,China [2]CAS Center For Excellence in Quantum Information and Quantum Physics,University of Science and Technology of China,Hefei 230026,China [3]Institute of Artificial Intelligence,Hefei Comprehensive National Science Center,Hefei 230026,China [4]Origin Quantum Co.,Ltd.(Hefei)

出　　处：《Chinese Physics Letters》2025年第3期16-22,共7页中国物理快报(英文版)

基　　金：supported by the National Key Research and Development Program of China (Grant No. 2023YFB4502500)。

摘　　要：Optimizing frequency configurations for qubits and gates in superconducting quantum chips presents a complex NP-complete challenge,critical for mitigating decoherence and crosstalk.This paper introduces a neural network-based approach,leveraging the network as a surrogate model to predict frequency errors.The method employs a closed-loop Bayesian optimization framework to iteratively refine configurations,guided by the network’s knowledge of nonlinear error mechanisms.By focusing on localized chip windows,the optimization identifies optimal frequency settings that minimize errors.The approach is validated through randomized and cross-entropy benchmarking,showing improved energy calculations when optimizing frequency configurations for a crosstalkaware hardware-efficient ansatz in variational quantum eigensolvers on superconducting quantum chips.

关 键 词：QUANTUM OPTIMIZATION VARIATIONAL 

分 类 号：O41[理学—理论物理]