论文标题
基于量子内核的机器学习方法的错误缓解IONQ和IBM量子计算机
Error mitigation for quantum kernel based machine learning methods on IonQ and IBM quantum computers
论文作者
论文摘要
内核方法是大多数古典机器学习算法的基础，例如高斯过程（GP）和支持向量机（SVM）。使用嘈杂的中间尺度量子（NISQ）设备计算内核，由于NISQ设备的设计最近进展，引起了很大的关注。但是，当前NISQ设备上的噪声和错误可能会对预测的内核产生负面影响。在本文中，我们利用两种量子内核机学习（ML）算法在两个不同的NISQ设备上预测乳腺癌数据集的标签：量子内核高斯工艺（QKGP）和量子内核支持向量机（QKSVM）。我们估计11量子IONQ和5个Qubit IBMQ BELEM量子设备上的量子内核。我们的结果表明，与非错误缓解量相比，误差缓解量的量子内核机学习算法的预测性能显着提高。在两个NISQ设备上，预测性能与无噪声量子模拟器及其经典同行相比
Kernel methods are the basis of most classical machine learning algorithms such as Gaussian Process (GP) and Support Vector Machine (SVM). Computing kernels using noisy intermediate scale quantum (NISQ) devices has attracted considerable attention due to recent progress in the design of NISQ devices. However noise and errors on current NISQ devices can negatively affect the predicted kernels. In this paper we utilize two quantum kernel machine learning (ML) algorithms to predict the labels of a Breast Cancer dataset on two different NISQ devices: quantum kernel Gaussian Process (qkGP) and quantum kernel Support Vector Machine (qkSVM). We estimate the quantum kernels on the 11 qubit IonQ and the 5 qubit IBMQ Belem quantum devices. Our results demonstrate that the predictive performances of the error mitigated quantum kernel machine learning algorithms improve significantly compared to their non-error mitigated counterparts. On both NISQ devices the predictive performances became comparable to those of noiseless quantum simulators and their classical counterparts
