On the challenges of using D-Wave computers to sample Boltzmann Random Variables

TL;DR

This paper discusses the significant challenges and obstacles in using D-Wave quantum computers to efficiently sample Boltzmann distributions, which are crucial for applications like Boltzmann machines.

Contribution

The paper provides a detailed analysis of the current difficulties and remaining obstacles in implementing Boltzmann sampling on D-Wave quantum hardware.

Findings

Identifies key technical challenges in D-Wave sampling

Explains why efficient Boltzmann sampling remains difficult

Highlights areas needing further research

Abstract

Sampling random variables following a Boltzmann distribution is an NP-hard problem involved in various applications such as training of \textit{Boltzmann machines}, a specific kind of neural network. Several attempts have been made to use a D-Wave quantum computer to sample such a distribution, as this could lead to significant speedup in these applications. Yet, at present, several challenges remain to efficiently perform such sampling. We detail the various obstacles and explain the remaining difficulties in solving the sampling problem on a D-wave machine.