  We introduce a hybrid model combining a quantum-inspired tensor network and a
variational quantum circuit to perform supervised learning tasks. This
architecture allows for the classical and quantum parts of the model to be
trained simultaneously, providing an end-to-end training framework. We show
that compared to the principal component analysis, a tensor network based on
the matrix product state with low bond dimensions performs better as a feature
extractor for the input data of the variational quantum circuit in the binary
and ternary classification of MNIST and Fashion-MNIST datasets. The
architecture is highly adaptable and the classical-quantum boundary can be
adjusted according the availability of the quantum resource by exploiting the
correspondence between tensor networks and quantum circuits.
