A Boltzmann Machine Implementation for the D-Wave

TL;DR

This paper demonstrates how the D-Wave quantum computer can be used as a hardware implementation of a Boltzmann machine, enabling neural network architectures with quantum-accelerated hidden layers for machine learning tasks.

Contribution

It presents a prototype of a multi-layer neural network utilizing the D-Wave as a quantum Boltzmann machine layer, exploring scalability and integration in neural networks.

Findings

Successful implementation of a 3-layer neural network with D-Wave as the hidden layer

Discussion on extending the approach to deeper and larger neural networks

Insights into the use of quantum hardware for machine learning models

Abstract

The D-Wave is an adiabatic quantum computer. It is an understatement to say that it is not a traditional computer. It can be viewed as a computational accelerator or more precisely a computational oracle, where one asks it a relevant question and it returns a useful answer. The question is how do you ask a relevant question and how do you use the answer it returns. This paper addresses these issues in a way that is pertinent to machine learning. A Boltzmann machine is implemented with the D-Wave since the D-Wave is merely a hardware instantiation of a partially connected Boltzmann machine. This paper presents a prototype implementation of a 3-layered neural network where the D-Wave is used as the middle (hidden) layer of the neural network. This paper also explains how the D-Wave can be utilized in a multi-layer neural network (more than 3 layers) and one in which each layer may be multiple times the size of the D-Wave being used.