A method for hybrid classical-quantum machine learning model training, the method using a hybrid classical-quantum computing system and comprising: receiving, by a first processor of the hybrid classical-quantum computing system, a training data set; selecting, by the first processor, a sampling of objects from the training set, each object represented by at least one vector, wherein the first processor is a classical processor preparing, by a quantum processor of the hybrid classical-quantum computing system, a plurality of different quantum feature map circuits (600, 700, 800, 900) corresponding to a plurality of different quantum kernel families, wherein each quantum feature map circuit (600, 700, 800, 900) of the plurality of different quantum feature map circuits (600, 700, 800, 900) has a different respective arrangement of quantum gates; identifying, from among the prepared plurality of different quantum feature map circuits (600, 700, 800, 900), a quantum feature map circuit (600, 700, 800, 900) that satisfies a threshold level of accuracy of a hybrid classical-quantum machine learning model, by performing one or more iterations of an optimization algorithm, wherein each iteration comprises: applying, by the quantum processor, a plurality of quantum feature maps to the selected sampling of objects, the plurality of quantum maps corresponding to the plurality of different quantum kernel families; evaluating, by the quantum processor, parameters for quantum logic gates of the plurality of different quantum feature map circuits (600, 700, 800, 900) each corresponding to at least one of the plurality of quantum feature maps; determining, by the first processor, a new set of parameters for the quantum feature map logic gates of a quantum feature map circuit (600, 700, 800, 900) of the quantum feature map Z circuits (600, 700, 800, 900); determining, by the first processor, whether the quantum feature map circuit Z (600, 700, 800, 900) with the new set of parameters for the quantum logic gates of the quantum feature map circuit (600, 700, 800, 900) produces the measure of accuracy of the hybrid classical-quantum machine learning model greater than a predetermined threshold value; and parameterizing, by the first processor, the quantum logic gates of the quantum feature map circuit (600, 700, 800, 900) with the new set of parameters for which the threshold of accuracy of the hybrid classical-quantum machine learning model is satisfied, wherein the machine learning model is based on the parameterized quantum feature map circuit. receiving, by a first processor of the hybrid classical-quantum computing system, a training data set; selecting, by the first processor, a sampling of objects from the training set, each object represented by at least one vector, wherein the first processor is a classical processor preparing, by a quantum processor of the hybrid classical-quantum computing system, a plurality of different quantum feature map circuits (600, 700, 800, 900) corresponding to a plurality of different quantum kernel families, wherein each quantum feature map circuit (600, 700, 800, 900) of the plurality of different quantum feature map circuits (600, 700, 800, 900) has a different respective arrangement of quantum gates; applying, by the quantum processor, a plurality of quantum feature maps to the selected sampling of objects, the plurality of quantum maps corresponding to the plurality of different quantum kernel families; evaluating, by the quantum processor, parameters for quantum logic gates of the plurality of different quantum feature map circuits (600, 700, 800, 900) each corresponding to at least one of the plurality of quantum feature maps; determining, by the first processor, a new set of parameters for the quantum feature map logic gates of a quantum feature map circuit (600, 700, 800, 900) of the quantum feature map Z circuits (600, 700, 800, 900); determining, by the first processor, whether the quantum feature map circuit Z (600, 700, 800, 900) with the new set of parameters for the quantum logic gates of the quantum feature map circuit (600, 700, 800, 900) produces the measure of accuracy of the hybrid classical-quantum machine learning model greater than a predetermined threshold value; and parameterizing, by the first processor, the quantum logic gates of the quantum feature map circuit (600, 700, 800, 900) with the new set of parameters for which the threshold of accuracy of the hybrid classical-quantum machine learning model is satisfied, wherein the machine learning model is based on the parameterized quantum feature map circuit.