  The field of Quantum Machine Learning (QML) has emerged recently in the hopes
of finding new machine learning protocols or exponential speedups for classical
ones. Apart from problems with vanishing gradients and efficient encoding
methods, these speedups are hard to find because the sampling nature of quantum
computers promotes either simulating computations classically or running them
many times on quantum computers in order to use approximate expectation values
in gradient calculations. In this paper, we make a case for setting high
single-sample accuracy as a primary goal. We discuss the statistical theory
which enables highly accurate and precise sample inference, and propose a
method of reversed training towards this end. We show the effectiveness of this
training method by assessing several effective variational quantum circuits
(VQCs), trained in both the standard and reversed directions, on random binary
subsets of the MNIST and MNIST Fashion datasets, on which our method provides
an increase of $10-15\%$ in single-sample inference accuracy.
