  Unsupervised machine learning models build an internal representation of
their training data without the need for explicit human guidance or feature
engineering. This learned representation provides insights into which features
of the data are relevant for the task at hand. In the context of quantum
physics, training models to describe quantum states without human intervention
offers a promising approach to gaining insight into how machines represent
complex quantum states. The ability to interpret the learned representation may
offer a new perspective on non-trivial features of quantum systems and their
efficient representation. We train a generative model on two-qubit density
matrices generated by a parameterized quantum circuit. In a series of
computational experiments, we investigate the learned representation of the
model and its internal understanding of the data. We observe that the model
learns an interpretable representation which relates the quantum states to
their underlying entanglement characteristics. In particular, our results
demonstrate that the latent representation of the model is directly correlated
with the entanglement measure concurrence. The insights from this study
represent proof of concept towards interpretable machine learning of quantum
states. Our approach offers insight into how machines learn to represent
small-scale quantum systems autonomously.
