论文标题
参数化量子电路中的信息流
Information flow in parameterized quantum circuits
论文作者
论文摘要
在这项工作中，我们引入了一种量化量子系统中信息流的新方法，尤其是对于参数化量子电路。我们使用电路的图表示，并使用门节点之间的相互信息提出了一个新的距离度量。然后，我们使用基于距离度量的路径为变分算法提供了一个优化过程。我们通过变异量子本质量探索算法的特征，其中我们计算海森伯格模型的基态能量。此外，我们采用了使用变异量子分类来解决二进制分类问题的方法。从数值模拟中，我们表明我们的方法可以成功地用于优化主要用于近期算法中的参数化量子电路。我们进一步注意到，基于信息流的路径可用于改善现有基于随机梯度的方法的收敛性。
In this work, we introduce a new way to quantify information flow in quantum systems, especially for parameterized quantum circuits. We use a graph representation of the circuits and propose a new distance metric using the mutual information between gate nodes. We then present an optimization procedure for variational algorithms using paths based on the distance measure. We explore the features of the algorithm by means of the variational quantum eigensolver, in which we compute the ground state energies of the Heisenberg model. In addition, we employ the method to solve a binary classification problem using variational quantum classification. From numerical simulations, we show that our method can be successfully used for optimizing the parameterized quantum circuits primarily used in near-term algorithms. We further note that information-flow based paths can be used to improve convergence of existing stochastic gradient based methods.
