{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/120332"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/120332","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Quantum algorithmic improvements for noisy intermediate scale quantum computers","abstract":"Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2025-05-01","abstract_html":"Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2025-05-01","abstract_has_math":false,"creators":["Slattery, Lucas"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Physics","degree_department":null,"school":null,"contributors":["Clark, Bryan","DeMarco, Brian","Kou, Angela","Pfaff, Wolfgang"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2023,"date_issued":"2023-05","date_published":"2023-05","updated_at":"2026-07-22T22:24:57Z","subjects":["Quantum Computing"],"languages":["en","eng"],"rights":["Copyright 2023 Lucas Slattery"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/120332","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Clark, Bryan","DeMarco, Brian","Kou, Angela","Pfaff, Wolfgang"]},{"key":"dc:creator","label":"Author","values":["Slattery, Lucas"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2023-05","2023-01-27"]},{"key":"dc:type","label":"Dc Type","values":["text","Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Physics"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Ph.D."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Quantum Computing"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en","eng"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2023 Lucas Slattery"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/120332"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["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","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."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Quantum algorithmic improvements for noisy intermediate scale quantum computers"]}]}],"canonical_facts":{"dc:contributor":["Clark, Bryan","DeMarco, Brian","Kou, Angela","Pfaff, Wolfgang"],"dc:creator":["Slattery, Lucas"],"dc:date":["2023-05","2023-01-27"],"dc:description":["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","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."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/120332"],"dc:language":["en","eng"],"dc:rights":["Copyright 2023 Lucas Slattery"],"dc:subject":["Quantum Computing"],"dc:title":["Quantum algorithmic improvements for noisy intermediate scale quantum computers"],"dc:type":["text","Thesis"],"thesis:degree_discipline":["Physics"],"thesis:degree_level":["Dissertation"],"thesis:degree_name":["Ph.D."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:24:57Z"}