Abstract

Through proofs and small scale implementations, quantum computing has shown potential to provide significant speedups in certain applications such as searches and matrix calculations. Recent library developments have introduced the concept of hybrid quantum-classical compute models where quantum processor units could be used as additional hardware accelerators by classical computers. While these developments have opened the prospect of applying quantum computing to machine learning tasks, there are still many limitations of near and midterm quantum computing. If implemented carefully, the advantages of quantum algorithms could be used to accelerate current machine learning models. In this work, a hybrid quantum-classical model is designed to solve a gradient descent problem. The quantum HHL algorithm is used to solve a system of linear equations. The quantum swap test circuit is then used to extract the Euclidean distance between a test point and the quantum solution. The Euclidean distance is then passed to a classical gradient descent algorithm to reduce the number of iterations required by the gradient descent algorithm to converge on a solution.

Library of Congress Subject Headings

Quantum computing; Neural networks (Computer science); Regression analysis

Publication Date

5-2023

Document Type

Thesis

Student Type

Graduate

Degree Name

Computer Engineering (MS)

Department, Program, or Center

Computer Engineering (KGCOE)

Advisor

Sonia Lopez Alarcon

Advisor/Committee Member

Corey Merkel

Advisor/Committee Member

Nathan Cahill

Campus

RIT – Main Campus

Plan Codes

CMPE-MS

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