  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.
