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We propose a novel algorithm for fast training of variational classifiers byprocessing multiple samples parallelly. The algorithm can be adapted for anyansatz used in the variational circuit. The presented algorithm utilizes qRAMand other quantum circuits in the forward pass. Further, instead of the usualpractice of computing the loss classically, we calculate the loss using aSwap-test circuit. The algorithm thus brings down the training cost of avariational classifier to O(logN)from the usual O(N)when training on a datasetof N samples. Although we discuss only binary classification in the paper, thealgorithm can be easily generalized to multi-class classification.

An Algorithm for Fast Supervised Learning in Variational Circuits through Simultaneous Processing of Multiple Samples