Quantum algorithmic improvements for noisy intermediate scale quantum computers

Slattery, Lucas

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https://hdl.handle.net/2142/120332 Copy

Description

Title

Quantum algorithmic improvements for noisy intermediate scale quantum computers

Author(s)

Slattery, Lucas

Issue Date

2023-01-27

Director of Research (if dissertation) or Advisor (if thesis)

Clark, Bryan

Doctoral Committee Chair(s)

DeMarco, Brian

Committee Member(s)

• Kou, Angela
• Pfaff, Wolfgang

Department of Study

Physics

Discipline

Physics

Degree Granting Institution

University of Illinois at Urbana-Champaign

Degree Name

Ph.D.

Degree Level

Dissertation

Keyword(s)

quantum computing

Abstract

The future of quantum computing promises a potential paradigm shift in computer science. In quantum computing hardware research, much effort and progress have been made in realizing quantum computers using silicon defects, ion traps, photonic chips, superconducting devices, and neutral atom traps. In quantum computing theory, there have been advances in device characterization, quantum complexity theory, and in novel algorithms for quantum computers. Quantum algorithms on quantum computers could help resolve previously intractable problems in disciplines ranging from finance to biology. However, when designing and applying quantum algorithms to solve any problem careful analysis is required in order to achieve an advantage over known classical algorithms. The work presented in this thesis represents several contributions to the careful design and analysis of quantum algorithms. In Chapter 2, I introduce a novel method for optimizing variational quantum circuits that mitigates many of the issues with training variational quantum circuits. In Chapter 3, I introduce a variational quantum circuit ansatz version of a two-dimensional tensor network and demonstrated its improved performance for two-dimensional physics problems. In Chapter 4, I perform a numerical study on the potential quantum advantage for a popular quantum machine learning model in the literature. Using analytical bounds, I demonstrate that for this particular model a quantum advantage is unlikely.

Graduation Semester

2023-05

Type of Resource

Thesis

Handle URL

https://hdl.handle.net/2142/120332

Copyright and License Information

Copyright 2023 Lucas Slattery

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