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We propose a new hybrid system for automatically generating and trainingquantum-inspired classifiers on grayscale images by using multiobjectivegenetic algorithms. We define a dynamic fitness function to obtain the smallestpossible circuit and highest accuracy on unseen data, ensuring that theproposed technique is generalizable and robust. We minimize the complexity ofthe generated circuits in terms of the number of entanglement gates bypenalizing their appearance. We reduce the size of the images with twodimensionality reduction approaches: principal component analysis (PCA), whichis encoded in the individual for optimization purpose, and a smallconvolutional autoencoder (CAE). These two methods are compared with oneanother and with a classical nonlinear approach to understand their behaviorsand to ensure that the classification ability is due to the quantum circuit andnot the preprocessing technique used for dimensionality reduction.

AutoQML: Automatic Generation and Training of Robust Quantum-Inspired Classifiers by Using Genetic Algorithms on Grayscale Images