论文标题
单量量子分类器的无梯度优化算法
Gradient-Free optimization algorithm for single-qubit quantum classifier
论文作者
论文摘要
在本文中，提出了一种无梯度量子分类器的无梯度优化算法，以克服由量子设备引起的贫瘠高原的影响。旋转门Rx（ϕ）应用于单量二进制量子分类器，并以矢量 - 义务的形式将训练数据和参数加载到ϕ中。通过找到每个参数的值来降低成本函数，从而产生测量量子电路的最低期望值。该算法是对所有参数迭代执行的，直到成本函数满足停止条件为止。为分类任务展示了所提出的算法，并将其与使用ADAM Optimizer进行了比较。此外，当量子设备中的旋转门在不同的噪声下时，讨论了与所提出的无梯度优化算法的单量量子分类器的性能。仿真结果表明，与使用ADAM Optimizer相比，具有建议的无梯度优化算法的单量量子分类器可以更快地达到高精度。此外，提出的无梯度优化算法可以快速完成单量分类器的训练过程。此外，具有提议的无梯度优化算法的单量量子分类器在嘈杂的环境中具有良好的性能。
In the paper, a gradient-free optimization algorithm for single-qubit quantum classifier is proposed to overcome the effects of barren plateau caused by quantum devices. A rotation gate RX(ϕ) is applied on a single-qubit binary quantum classifier, and the training data and parameters are loaded into ϕ with the form of vector-multiplication. The cost function is decreased by finding the value of each parameter that yield the minimum expectation value of measuring the quantum circuit. The algorithm is performed iteratively for all parameters one by one, until the cost function satisfies the stop condition. The proposed algorithm is demonstrated for a classification task and is compared with that using Adam optimizer. Furthermore, the performance of the single-qubit quantum classifier with the proposed gradient-free optimization algorithm is discussed when the rotation gate in quantum device is under different noise. The simulation results show that the single-qubit quantum classifier with proposed gradient-free optimization algorithm can reach a high accuracy faster than that using Adam optimizer. Moreover, the proposed gradient-free optimization algorithm can quickly completes the training process of the single-qubit classifier. Additionally, the single-qubit quantum classifier with proposed gradient-free optimization algorithm has a good performance in noisy environments.
