Work

Solving machine learning optimization problems using quantum computers

Year: 2020

Type: conference-paper

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 to a 3-dimensional image that vary with time as well as tracking speed in object identification.

Source: Disruptive Technologies in Information Sciences

DOI: 10.1117/12.2565038

Authors: Venkat Dasari, Mee Seong Im, Lubjana Beshaj

Institutions: DEVCOM Army Research Laboratory, United States Army, United States Military Academy, Army Cyber Institute at West Point

Language: English

Topic: Quantum Computing Algorithms and Architecture

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W2991643983

FWCI: 0.32

Cites: 11

Cited by: 11

Related to: 10