Theses and Dissertations

Advisor

Perkins, Andy

Committee Member

Novotny, Mark

Committee Member

Rahimi, Shahram

Committee Member

Mittal, Sudip

Date of Degree

8-7-2025

Original embargo terms

Immediate Worldwide Access

Document Type

Dissertation - Open Access

Major

Computer Science

Degree Name

Doctor of Philosophy (Ph.D.)

College

James Worth Bagley College of Engineering

Department

Department of Computer Science and Engineering

Abstract

This dissertation explores the methodology for more thoroughly entangling artificial intelligence and quantum computing. This is explored through a background search of problems solved using Grover’s quantum algorithm, a new quantum protein folding and drug discovery algorithm, using Grover’s algorithm to train quantum artificial neural networks, using quantum artificial neural networks for reinforcement learning, mapping classical assembly instructions to quantum circuits to make quantum programming easier, and using prompt engineering to get a classical artificial intelligence agent to solve an NP-Complete problem using a Grover’s algorithm. This method not only simplifies the creation of algorithms but also opens new avenues in quantum algorithm design. The findings contribute to the entanglement of classical and quantum computing and AI-enhanced quantum computing, thereby advancing the field of quantum information science.

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