论文标题
AutoQML：通过在灰度图像上使用遗传算法的自动生成和培训强大的量子启发的分类器
AutoQML: Automatic Generation and Training of Robust Quantum-Inspired Classifiers by Using Genetic Algorithms on Grayscale Images
论文作者
论文摘要
我们提出了一种新的混合系统，用于通过使用多目标遗传算法在灰度图像上自动生成和训练量子启发的分类器。我们定义动态健身函数，以获得最小的电路和最高的数据，以确保所提出的技术是可推广且稳健的。我们通过惩罚其外观来最大程度地减少生成电路的复杂性。我们使用二维降低方法减少图像的大小：主成分分析（PCA），该分析（PCA）是为了优化目的而在个人中编码的，以及一个小的卷积自动编码器（CAE）。将这两种方法相互比较，并采用经典的非线性方法来了解其行为，并确保分类能力是由于量子电路而不是用于降低维度的预处理技术引起的。
We propose a new hybrid system for automatically generating and training quantum-inspired classifiers on grayscale images by using multiobjective genetic algorithms. We define a dynamic fitness function to obtain the smallest possible circuit and highest accuracy on unseen data, ensuring that the proposed technique is generalizable and robust. We minimize the complexity of the generated circuits in terms of the number of entanglement gates by penalizing their appearance. We reduce the size of the images with two dimensionality reduction approaches: principal component analysis (PCA), which is encoded in the individual for optimization purpose, and a small convolutional autoencoder (CAE). These two methods are compared with one another and with a classical nonlinear approach to understand their behaviors and to ensure that the classification ability is due to the quantum circuit and not the preprocessing technique used for dimensionality reduction.
