  The potential impact of quantum machine learning algorithms on industrial
applications remains an exciting open question. Conventional methods for
encoding classical data into quantum computers are not only too costly for a
potential quantum advantage in the algorithms but also severely limit the scale
of feasible experiments on current hardware. Therefore, recent works, despite
claiming the near-term suitability of their algorithms, do not provide
experimental benchmarking on standard machine learning datasets. We attempt to
solve the data encoding problem by improving a recently proposed variational
algorithm [1] that approximately prepares the encoded data, using
asymptotically shallow circuits that fit the native gate set and topology of
currently available quantum computers. We apply the improved algorithm to
encode the Fashion-MNIST dataset [2], which can be directly used in future
empirical studies of quantum machine learning algorithms. We deploy simple
quantum variational classifiers trained on the encoded dataset on a current
quantum computer ibmq-kolkata [3] and achieve moderate accuracies, providing a
proof of concept for the near-term usability of our data encoding method.
