2026-09-21T10:19:26Z https://www.ideals.illinois.edu/oai-pmh

oai:www.ideals.illinois.edu:2142/120332 2023-09-05 col_2142_5131 col_2142_8859 com_2142_5130 com_2142_8858 com_2142_234

Clark, Bryan DeMarco, Brian Kou, Angela Pfaff, Wolfgang 2023-05 application/pdf en text Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2025-05-01 The student, Lucas Slattery, accepted the attached license on 2023-01-11 at 13:26. The student, Lucas Slattery, submitted this Dissertation for approval on 2023-01-13 at 09:43. This Dissertation was approved for publication on 2023-01-27 at 13:28. DSpace SAF Submission Ingestion Package generated from Vireo submission #18821 on 2023-09-01 at 17:12:31 Quantum algorithmic improvements for noisy intermediate scale quantum computers Slattery, Lucas 2023-01-27 Quantum Computing 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. Thesis eng https://hdl.handle.net/2142/120332 Copyright 2023 Lucas Slattery Ph.D. Dissertation Physics University of Illinois at Urbana-Champaign Physics