  The promise of quantum neural nets, which utilize quantum effects to model
complex data sets, has made their development an aspirational goal for quantum
machine learning and quantum computing in general. Here we provide new methods
of training quantum Boltzmann machines, which are a class of recurrent quantum
neural network. Our work generalizes existing methods and provides new
approaches for training quantum neural networks that compare favorably to
existing methods. We further demonstrate that quantum Boltzmann machines enable
a form of quantum state tomography that not only estimates a state but provides
a perscription for generating copies of the reconstructed state. Classical
Boltzmann machines are incapable of this. Finally we compare small
non-stoquastic quantum Boltzmann machines to traditional Boltzmann machines for
generative tasks and observe evidence that quantum models outperform their
classical counterparts.
