Encoding hyperspectral data with low-bond dimension quantum tensor networks

Abstract

Encoding data on a quantum computer poses a major challenge on data intensive quantum applications like machine learning. In particular, data with complex internal structure like emission spectra need to be adapted to reduce the encoding effort of quantum circuits. We empirically investigate the influence of compression on the encoding of hyperspectral data into quantum states, to make its encoding more efficient. To this end, we assess the effect of approximating states by low-bond dimension matrix product states fed into a variational quantum classifier on the public Pavia University benchmark dataset.