Quantum Deep Learning

TL;DR

This paper explores how quantum computing can enhance deep learning by reducing training times and enabling more complex models, surpassing classical methods in efficiency and capability.

Contribution

It introduces quantum algorithms for deep learning models, demonstrating significant improvements over classical approaches in training and optimization.

Findings

Quantum algorithms reduce training time for deep models.

Quantum methods enable training of more complex deep models.

Significant optimization improvements over classical methods.

Abstract

In recent years, deep learning has had a profound impact on machine learning and artificial intelligence. At the same time, algorithms for quantum computers have been shown to efficiently solve some problems that are intractable on conventional, classical computers. We show that quantum computing not only reduces the time required to train a deep restricted Boltzmann machine, but also provides a richer and more comprehensive framework for deep learning than classical computing and leads to significant improvements in the optimization of the underlying objective function. Our quantum methods also permit efficient training of full Boltzmann machines and multi-layer, fully connected models and do not have well known classical counterparts.