Information Perspective to Probabilistic Modeling: Boltzmann Machines versus Born Machines
Abstract
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 energybased 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.
 Publication:

Entropy
 Pub Date:
 August 2018
 DOI:
 10.3390/e20080583
 arXiv:
 arXiv:1712.04144
 Bibcode:
 2018Entrp..20..583C
 Keywords:

 Physics  Data Analysis;
 Statistics and Probability;
 Condensed Matter  Statistical Mechanics;
 Quantum Physics;
 Statistics  Machine Learning
 EPrint:
 7 pages, 4 figures