  Tensor Networks are non-trivial representations of high-dimensional tensors,
originally designed to describe quantum many-body systems. We show that Tensor
Networks are ideal vehicles to connect quantum mechanical concepts to machine
learning techniques, thereby facilitating an improved interpretability of
neural networks. This study presents the discrimination of top quark signal
over QCD background processes using a Matrix Product State classifier. We show
that entanglement entropy can be used to interpret what a network learns, which
can be used to reduce the complexity of the network and feature space without
loss of generality or performance. For the optimisation of the network, we
compare the Density Matrix Renormalization Group (DMRG) algorithm to stochastic
gradient descent (SGD) and propose a joined training algorithm to harness the
explainability of DMRG with the efficiency of SGD.
