论文标题
基于内核的量子回归模型学习非马克维亚性
Kernel-based quantum regressor models learn non-Markovianity
论文作者
论文摘要
Quantum机器学习是一个不断增长的研究领域，旨在执行量子计算机协助的机器学习任务。基于内核的量子机学习模型是范式涉及量子状态的范式示例，并且从这些状态之间的重叠中计算出革兰氏矩阵。在手头内核的情况下，将常规的机器学习模型用于学习过程。在本文中，我们研究了量子支持向量机和量子内核脊模型，以预测量子系统的非马克维亚性程度。我们对幅度阻尼和相阻尼通道进行数字量子模拟，以创建我们的量子数据集。我们详细介绍了不同的内核函数，以绘制数据和内核电路以计算量子状态之间的重叠。我们表明，我们的模型提供了与完全经典模型相当的准确预测。
Quantum machine learning is a growing research field that aims to perform machine learning tasks assisted by a quantum computer. Kernel-based quantum machine learning models are paradigmatic examples where the kernel involves quantum states, and the Gram matrix is calculated from the overlap between these states. With the kernel at hand, a regular machine learning model is used for the learning process. In this paper we investigate the quantum support vector machine and quantum kernel ridge models to predict the degree of non-Markovianity of a quantum system. We perform digital quantum simulation of amplitude damping and phase damping channels to create our quantum dataset. We elaborate on different kernel functions to map the data and kernel circuits to compute the overlap between quantum states. We show that our models deliver accurate predictions that are comparable with the fully classical models.
