  We propose a novel algorithm for fast training of variational classifiers by
processing multiple samples parallelly. The algorithm can be adapted for any
ansatz used in the variational circuit. The presented algorithm utilizes qRAM
and other quantum circuits in the forward pass. Further, instead of the usual
practice of computing the loss classically, we calculate the loss using a
Swap-test circuit. The algorithm thus brings down the training cost of a
variational classifier to O(logN)from the usual O(N)when training on a dataset
of N samples. Although we discuss only binary classification in the paper, the
algorithm can be easily generalized to multi-class classification.
