Doctoral Dissertations

Date of Award

12-2023

Degree Type

Dissertation

Degree Name

Doctor of Philosophy

Major

Mathematics

Major Professor

Juan M. Restrepo

Committee Members

Suzanne Lenhart, Vicente Leyton-Ortega, Vasileios Maroulas

Abstract

In the burgeoning field of quantum machine learning, the fusion of quantum computing and machine learning methodologies has sparked immense interest, particularly with the emergence of noisy intermediate-scale quantum (NISQ) devices. These devices hold the promise of achieving quantum advantage, but they grapple with limitations like constrained qubit counts, limited connectivity, operational noise, and a restricted set of operations. These challenges necessitate a strategic and deliberate approach to crafting effective quantum machine learning algorithms.

This dissertation revolves around an exploration of these challenges, presenting innovative strategies that tailor quantum algorithms and processes to seamlessly integrate with commercial quantum platforms. A pivotal proposition within this framework is the creation of a resource-efficient quantum generative adversarial network (QGAN). This adaptation of QGANs, which are typically used to synthesize data according to specific probability distributions, ensures optimal performance even within environments with limited resources.

Furthermore, our research delves deeply into the development of a data encoding procedure specifically designed for NISQ devices. This encoding process, responsible for translating classical data into quantum states to enable quantum algorithm processing, plays a critical role in quantum machine learning. Our goal is to establish an encoding approach that optimally utilizes quantum resources while mitigating the impact of noise and inherent limitations in NISQ devices.

Another key aspect of our study is the seamless alignment of the devised algorithms with the existing architectures of NISQ devices. Given the pivotal role of these devices in contemporary quantum technology, ensuring compatibility is of utmost importance. This not only facilitates immediate applications but also establishes a robust framework for accommodating future technological advancements.

Through an extensive analysis of these critical dimensions, our objective is to make a substantial contribution to the practical implementation of quantum machine learning algorithms on commercial quantum platforms. We aim to navigate the intricate landscape of NISQ technologies adeptly, thereby facilitating the seamless integration of quantum machine learning into real-world applications. Ultimately, this research endeavor aspires to drive advancements at the nexus of quantum computing and machine learning.

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