  Quantum physics experiments produce interesting phenomena such as
interference or entanglement, which are core properties of numerous future
quantum technologies. The complex relationship between the setup structure of a
quantum experiment and its entanglement properties is essential to fundamental
research in quantum optics but is difficult to intuitively understand. We
present a deep generative model of quantum optics experiments where a
variational autoencoder is trained on a dataset of quantum optics experimental
setups. In a series of computational experiments, we investigate the learned
representation of our Quantum Optics Variational Auto Encoder (QOVAE) and its
internal understanding of the quantum optics world. We demonstrate that the
QOVAE learns an interpretable representation of quantum optics experiments and
the relationship between experiment structure and entanglement. We show the
QOVAE is able to generate novel experiments for highly entangled quantum states
with specific distributions that match its training data. The QOVAE can learn
to generate specific entangled states and efficiently search the space of
experiments that produce highly entangled quantum states. Importantly, we are
able to interpret how the QOVAE structures its latent space, finding curious
patterns that we can explain in terms of quantum physics. The results
demonstrate how we can use and understand the internal representations of deep
generative models in a complex scientific domain. The QOVAE and the insights
from our investigations can be immediately applied to other physical systems.
