  This work presents a novel realization approach to Quantum Boltzmann Machines
(QBMs). The preparation of the required Gibbs states, as well as the evaluation
of the loss function's analytic gradient is based on Variational Quantum
Imaginary Time Evolution, a technique that is typically used for ground state
computation. In contrast to existing methods, this implementation facilitates
near-term compatible QBM training with gradients of the actual loss function
for arbitrary parameterized Hamiltonians which do not necessarily have to be
fully-visible but may also include hidden units. The variational Gibbs state
approximation is demonstrated with numerical simulations and experiments run on
real quantum hardware provided by IBM Quantum. Furthermore, we illustrate the
application of this variational QBM approach to generative and discriminative
learning tasks using numerical simulation.
