Solving machine learning optimization problems using quantum computers

TL;DR

This paper explores how quantum computing can accelerate machine learning optimization by leveraging quantum parallelism, demonstrated through a model applied to a 3D time-varying image.

Contribution

It introduces a generic mathematical model for using quantum parallelism to enhance machine learning optimization processes.

Findings

Quantum parallelism can significantly speed up optimization tasks.

A practical application to a 3D time-varying image demonstrates the approach.

The model shows potential for reducing computational resource requirements.

Abstract

Classical optimization algorithms in machine learning often take a long time to compute when applied to a multi-dimensional problem and require a huge amount of CPU and GPU resource. Quantum parallelism has a potential to speed up machine learning algorithms. We describe a generic mathematical model to leverage quantum parallelism to speed-up machine learning algorithms. We also apply quantum machine learning and quantum parallelism applied to a $3$-dimensional image that vary with time.