{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/129329"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/129329","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"A quantum optimization algorithm for optimal electric vehicle charging station placement for intercity trips","abstract":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-10-19 without embargo terms","abstract_html":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-10-19 without embargo terms","abstract_has_math":false,"creators":["Radvand, Tina"],"institution":"University of Illinois Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Civil Engineering","degree_department":null,"school":null,"contributors":["Talebpour, Alireza"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2025,"date_issued":"2025-05-06","date_published":"2025-05-06","updated_at":"2026-07-22T22:25:04Z","subjects":["Electric Vehicle","Charging Station Location","Grover's Adaptive Search","Quantum Optimization"],"languages":["en","eng"],"rights":["Copyright © 2025 Tina Radvand"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/129329","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Talebpour, Alireza"]},{"key":"dc:creator","label":"Author","values":["Radvand, Tina"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2025-05-06","2025-05"]},{"key":"dc:type","label":"Dc Type","values":["text","Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Civil Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Electric Vehicle","Charging Station Location","Grover's Adaptive Search","Quantum Optimization"]}]},{"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 © 2025 Tina Radvand"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/129329"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-10-19 without embargo terms","The student, Tina Radvand, accepted the attached license on 2025-05-05 at 16:41.","The student, Tina Radvand, submitted this Thesis for approval on 2025-05-05 at 16:55.","This Thesis was approved for publication on 2025-05-06 at 16:35.","DSpace SAF Submission Ingestion Package generated from Vireo submission #22221 on 2025-10-19 at 18:13:06","Electric vehicles (EVs) play a significant role in enhancing the sustainability of transportation systems. However, their widespread adoption is hindered by inadequate public charging infrastructure, particularly to support long-distance travel. Identifying optimal charging station locations in large transportation networks presents an NP-hard combinatorial optimization problem, as the search space grows exponentially with the number of potential charging station locations. This thesis introduces a quantum search-based optimization algorithm designed to enhance the efficiency of solving this NP-hard problem for transportation networks. By leveraging quantum parallelism, amplitude amplification, and quantum phase estimation as a subroutine, the optimal solution is identified with a quadratic improvement in complexity compared to classical exact methods, such as branch and bound. The detailed design of a quantum circuit for optimizing the charging station locations is presented, and the complexity of the proposed quantum algorithm is analyzed."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["A quantum optimization algorithm for optimal electric vehicle charging station placement for intercity trips"]}]}],"canonical_facts":{"dc:contributor":["Talebpour, Alireza"],"dc:creator":["Radvand, Tina"],"dc:date":["2025-05-06","2025-05"],"dc:description":["Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-10-19 without embargo terms","The student, Tina Radvand, accepted the attached license on 2025-05-05 at 16:41.","The student, Tina Radvand, submitted this Thesis for approval on 2025-05-05 at 16:55.","This Thesis was approved for publication on 2025-05-06 at 16:35.","DSpace SAF Submission Ingestion Package generated from Vireo submission #22221 on 2025-10-19 at 18:13:06","Electric vehicles (EVs) play a significant role in enhancing the sustainability of transportation systems. However, their widespread adoption is hindered by inadequate public charging infrastructure, particularly to support long-distance travel. Identifying optimal charging station locations in large transportation networks presents an NP-hard combinatorial optimization problem, as the search space grows exponentially with the number of potential charging station locations. This thesis introduces a quantum search-based optimization algorithm designed to enhance the efficiency of solving this NP-hard problem for transportation networks. By leveraging quantum parallelism, amplitude amplification, and quantum phase estimation as a subroutine, the optimal solution is identified with a quadratic improvement in complexity compared to classical exact methods, such as branch and bound. The detailed design of a quantum circuit for optimizing the charging station locations is presented, and the complexity of the proposed quantum algorithm is analyzed."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/129329"],"dc:language":["en","eng"],"dc:rights":["Copyright © 2025 Tina Radvand"],"dc:subject":["Electric Vehicle","Charging Station Location","Grover's Adaptive Search","Quantum Optimization"],"dc:title":["A quantum optimization algorithm for optimal electric vehicle charging station placement for intercity trips"],"dc:type":["text","Thesis"],"thesis:degree_discipline":["Civil Engineering"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["M.S."],"thesis:institution_name":["University of Illinois Urbana-Champaign"]},"updated_at":"2026-07-22T22:25:04Z"}