基于SVM的量子线路输出校准方法研究

Research on calibration method of quantum circuit output based on SVM

当前噪声中尺度量子(NISQ)计算机由于受到各种噪声的影响,量子线路运行结果和理想结果之间存在误差,因此需要对量子线路的运行结果进行校准。基于量子线路可逆性的特点,收集正反向线路运行数据中的状态误差作为主要噪声特征,提出了基于支持向量机(SVM)集成策略的输出校准方法。通过支持向量机-递归特征消除(SVM-RFE)方法对噪声特征进行排序,去除过拟合的特征,从而得到更优的量子线路输出校准结果。实验结果表明,与基于优化映射的方法相比,所提基于SVM的方法使量子线路输出结果更接近于理想结果,与基于决策树集成分类模型(Qraft)相比,当CNOT量子线路的门数为60时,改善率达到43.94%。

Current noisy intermediate-scale quantum(NISQ)computers are subject to various noises,resulting in the errors between the quantum circuit operation results and the ideal results.In order to make the circuit output closer to the desired result,the operation results of the quantum circuit need to be calibrated.Based on the reversibility of quantum circuit,the state errors in the forward and reverse circuit operation data are collected as the main noise features,and then an output calibration method based on support vector machine(SVM)is proposed.According to the method,the noise characteristics are sorted firstly by the support vector machine-recursive feature elimination(SVM-RFE)method,then the overfitted features are removed to obtain better calibration results for quantum circuit output.Experimental results show that compared with the optimization-based mapping methods,the proposed SVM-based method yields quantum circuit output results that are closer to the ideal results.In comparison to the decision tree ensemble classification model(Qraft),when the gate count of the CNOT quantum circuit is 60,the improvement rate reaches 43.94%.