Learning Restricted Boltzmann Machines with greedy quantum search
Liming Zhao Aman Agrawal Patrick Rebentrost
Restricted Boltzmann Machines (RBMs) are widely used probabilistic undirected
graphical models with visible and latent nodes, playing an important role in
statistics and machine learning. The task of structure learning for RBMs
involves inferring the underlying graph by using samples from the visible
nodes. Specifically, learning the two-hop neighbors of each visible node allows
for the inference of the graph structure. Prior research has addressed the
structure learning problem for specific classes of RBMs, namely ferromagnetic
and locally consistent RBMs. In this paper, we extend the scope to the quantum
computing domain and propose corresponding quantum algorithms for this problem.
Our study demonstrates that the proposed quantum algorithms yield a polynomial
speedup compared to the classical algorithms for learning the structure of
these two classes of RBMs.