  Recent work suggests that quantum machine learning techniques can be used for
classical image classification by encoding the images in quantum states and
using a quantum neural network for inference. However, such work has been
restricted to very small input images, at most 4 x 4, that are unrealistic and
cannot even be accurately labeled by humans. The primary difficulties in using
larger input images is that hitherto-proposed encoding schemes necessitate more
qubits than are physically realizable. We propose a framework to classify
larger, realistic images using quantum systems. Our approach relies on a novel
encoding mechanism that embeds images in quantum states while necessitating
fewer qubits than prior work. Our framework is able to classify images that are
larger than previously possible, up to 16 x 16 for the MNIST dataset on a
personal laptop, and obtains accuracy comparable to classical neural networks
with the same number of learnable parameters. We also propose a technique for
further reducing the number of qubits needed to represent images that may
result in an easier physical implementation at the expense of final
performance. Our work enables quantum machine learning and classification on
classical datasets of dimensions that were previously intractable by physically
realizable quantum computers or classical simulation
