Training Ensembles of Quantum Binary Neural Networks

Artificial Neural Networks (NNs) are applied to solve problems in several fields such as translation, image processing, and diagnosis of diseases. Training NNs and searching weights parameters for them is a costly task that depends on weights initialization. This problem cost increases when it is necessary to train an Ensemble of Neural Networks (ENNs). Some works explore ways to train Binary Neural Networks (BNNs) using quantum techniques with success. This paper shows a hybrid approach to train an Ensemble of Binary Neural Networks with the same computational cost to train one variational quantum circuit.