  We compare and contrast the statistical physics and quantum physics inspired
approaches for unsupervised generative modeling of classical data. The two
approaches represent probabilities of observed data using energy-based models
and quantum states respectively.Classical and quantum information patterns of
the target datasets therefore provide principled guidelines for structural
design and learning in these two approaches. Taking the restricted Boltzmann
machines (RBM) as an example, we analyze the information theoretical bounds of
the two approaches. We verify our reasonings by comparing the performance of
RBMs of various architectures on the standard MNIST datasets.
