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.

本研究提出了一个新的混合系统，通过使用多目标遗传算法，自动生成和训练基于量子启发式分类器来处理灰度图像。通过定义动态适应性函数来确保所提出的技术具有通用性和鲁棒性，我们能够在视觉数据集上获得最小电路和最高精度，同时在量子电路和图像降维方法之间进行比较。

AutoQML：使用遗传算法在灰度图像上自动生成和训练鲁棒的量子启发式分类器