Toward the Implementation of a Quantum RBM

Conference item

Quantum computers promise the ability to solve many types of difficult computational problems efficiently. It turns out that Boltzmann Machines are ideal candidates for implementation on a quantum computer, due to their close relationship to the Ising model from statistical physics. In this paper we describe how to use quantum hardware to train Boltzmann Machines with connections between latent units. We also describe the architecture we are targeting and discuss difficulties we face in applying the current generation of quantum computers to this hard problem.

Authors: Denil, M; de Freitas, N

• Host title: NIPS 2011 Deep Learning and Unsupervised Feature Learning Workshop
• Publication date: 2011-01-01