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.

本文提出一种基于虚时间演化的变分量子 Gibbs 状态逼近方法，实现了与临近中期的量子计算机兼容的量子 Boltzmann 机械训练方法，同时支持任意参数哈密顿量的梯度下降求解，适用于生成学习与判别学习任务。