{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/132520"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/132520","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Hybrid classical-quantum optimization algorithms in electromagnetic applications","abstract":"Physics-based computing based on Ising models has emerged as a powerful paradigm for tackling large-scale combinatorial optimization problems. This dissertation develops a unified Ising-based framework for electromagnetic design, with a particular focus on reconfigurable intelligent surfaces (RISs) and antenna array synthesis. A broad class of electromagnetic tasks—including discrete-phase RIS configuration, antenna beamforming and null steering for multi-user links and wireless power transfer, and planar or conformal array synthesis under quantized amplitude and phase constraints—is reformulated as Ising Hamiltonians. On the algorithmic side, the work investigates both quantum and classical physics-based Ising solvers. Quantum annealing is implemented on D-Wave quantum processing units (QPUs) and used to solve medium-scale instances, and a hybrid classical–quantum divide-and-conquer scheme is developed that decomposes large dense problems into QPU-sized subproblems, which are solved on the annealer and reconciled by classical post-processing. These studies clarify the strengths of quantum annealing and hybrid workflows, as well as current limitations in qubit count and sparse hardware connectivity for very large electromagnetic problems. Complementing the quantum approaches, the dissertation also considers classical physics-inspired Ising machines, in which the dynamics of nonlinear oscillators are emulated on conventional CPUs and GPUs. Among these, I focus on the simulated bifurcation (SB) algorithm, which searches for low-energy Ising states by driving a network of coupled oscillators through a controlled bifurcation process. My core contribution is a detailed nonlinear-dynamics analysis of SB based on an eigenvalue stability framework, which explains how pump schedules govern mode bifurcation and reveals bottlenecks such as retarded bifurcation. Guided by these insights, I introduce time-modulated simulated bifurcation (TM-SB), in which system parameters that are fixed in the classical formulation are deliberately modulated over time, together with a momentum-aware pumping strategy. This time modulation makes the dynamics much more likely to settle into high-quality minima, reducing trapping in shallow local optima and reducing the number of runs needed to obtain good solutions. Numerical studies demonstrate that TM-SB and related Ising-based solvers offer robust performance and strong scalability across these scenarios, often matching or surpassing the best-known classical baselines. Overall, the results position physics-based, Ising-model computing—and TM-SB in particular—as a practical and scalable pathway toward adaptive, energy-efficient smart radio environments and intelligent electromagnetic systems.","abstract_html":"Physics-based computing based on Ising models has emerged as a powerful paradigm for tackling large-scale combinatorial optimization problems. This dissertation develops a unified Ising-based framework for electromagnetic design, with a particular focus on reconfigurable intelligent surfaces (RISs) and antenna array synthesis. A broad class of electromagnetic tasks—including discrete-phase RIS configuration, antenna beamforming and null steering for multi-user links and wireless power transfer, and planar or conformal array synthesis under quantized amplitude and phase constraints—is reformulated as Ising Hamiltonians. On the algorithmic side, the work investigates both quantum and classical physics-based Ising solvers. Quantum annealing is implemented on D-Wave quantum processing units (QPUs) and used to solve medium-scale instances, and a hybrid classical–quantum divide-and-conquer scheme is developed that decomposes large dense problems into QPU-sized subproblems, which are solved on the annealer and reconciled by classical post-processing. These studies clarify the strengths of quantum annealing and hybrid workflows, as well as current limitations in qubit count and sparse hardware connectivity for very large electromagnetic problems. Complementing the quantum approaches, the dissertation also considers classical physics-inspired Ising machines, in which the dynamics of nonlinear oscillators are emulated on conventional CPUs and GPUs. Among these, I focus on the simulated bifurcation (SB) algorithm, which searches for low-energy Ising states by driving a network of coupled oscillators through a controlled bifurcation process. My core contribution is a detailed nonlinear-dynamics analysis of SB based on an eigenvalue stability framework, which explains how pump schedules govern mode bifurcation and reveals bottlenecks such as retarded bifurcation. Guided by these insights, I introduce time-modulated simulated bifurcation (TM-SB), in which system parameters that are fixed in the classical formulation are deliberately modulated over time, together with a momentum-aware pumping strategy. This time modulation makes the dynamics much more likely to settle into high-quality minima, reducing trapping in shallow local optima and reducing the number of runs needed to obtain good solutions. Numerical studies demonstrate that TM-SB and related Ising-based solvers offer robust performance and strong scalability across these scenarios, often matching or surpassing the best-known classical baselines. Overall, the results position physics-based, Ising-model computing—and TM-SB in particular—as a practical and scalable pathway toward adaptive, energy-efficient smart radio environments and intelligent electromagnetic systems.","abstract_has_math":false,"creators":["Lim, Qi Jian"],"institution":"University of Illinois Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Electrical & Computer Engr","degree_department":null,"school":null,"contributors":["Peng, Zhen","Jin, Jian-Ming","Schutt-Aine, Jose","Soltanaghai, Elahé"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2025,"date_issued":"2025-12","date_published":"2025-12","updated_at":"2026-07-22T22:25:07Z","subjects":["Reconfigurable Intelligent Surfaces","Time-Modulated Simulated Bifurcation","Ising-based optimization","Antenna arrays and beamforming","Hybrid classical–quantum optimization"],"languages":["en"],"rights":["© 2025 Qi Jian Lim"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/132520","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Peng, Zhen","Jin, Jian-Ming","Schutt-Aine, Jose","Soltanaghai, Elahé"]},{"key":"dc:creator","label":"Author","values":["Lim, Qi Jian"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2025-12","2025-11-24"]},{"key":"dc:type","label":"Dc Type","values":["text","Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Electrical & Computer Engr"]},{"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 Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Reconfigurable Intelligent Surfaces","Time-Modulated Simulated Bifurcation","Ising-based optimization","Antenna arrays and beamforming","Hybrid classical–quantum optimization"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["© 2025 Qi Jian Lim"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/132520"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Physics-based computing based on Ising models has emerged as a powerful paradigm for tackling large-scale combinatorial optimization problems. This dissertation develops a unified Ising-based framework for electromagnetic design, with a particular focus on reconfigurable intelligent surfaces (RISs) and antenna array synthesis. A broad class of electromagnetic tasks—including discrete-phase RIS configuration, antenna beamforming and null steering for multi-user links and wireless power transfer, and planar or conformal array synthesis under quantized amplitude and phase constraints—is reformulated as Ising Hamiltonians. On the algorithmic side, the work investigates both quantum and classical physics-based Ising solvers. Quantum annealing is implemented on D-Wave quantum processing units (QPUs) and used to solve medium-scale instances, and a hybrid classical–quantum divide-and-conquer scheme is developed that decomposes large dense problems into QPU-sized subproblems, which are solved on the annealer and reconciled by classical post-processing. These studies clarify the strengths of quantum annealing and hybrid workflows, as well as current limitations in qubit count and sparse hardware connectivity for very large electromagnetic problems. Complementing the quantum approaches, the dissertation also considers classical physics-inspired Ising machines, in which the dynamics of nonlinear oscillators are emulated on conventional CPUs and GPUs. Among these, I focus on the simulated bifurcation (SB) algorithm, which searches for low-energy Ising states by driving a network of coupled oscillators through a controlled bifurcation process. My core contribution is a detailed nonlinear-dynamics analysis of SB based on an eigenvalue stability framework, which explains how pump schedules govern mode bifurcation and reveals bottlenecks such as retarded bifurcation. Guided by these insights, I introduce time-modulated simulated bifurcation (TM-SB), in which system parameters that are fixed in the classical formulation are deliberately modulated over time, together with a momentum-aware pumping strategy. This time modulation makes the dynamics much more likely to settle into high-quality minima, reducing trapping in shallow local optima and reducing the number of runs needed to obtain good solutions. Numerical studies demonstrate that TM-SB and related Ising-based solvers offer robust performance and strong scalability across these scenarios, often matching or surpassing the best-known classical baselines. Overall, the results position physics-based, Ising-model computing—and TM-SB in particular—as a practical and scalable pathway toward adaptive, energy-efficient smart radio environments and intelligent electromagnetic systems.","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2026-02-19 without embargo terms","The student, Qi Jian Lim, accepted the attached license on 2025-11-21 at 06:28.","The student, Qi Jian Lim, submitted this Dissertation for approval on 2025-11-21 at 07:49.","This Dissertation was approved for publication on 2025-11-24 at 10:17.","DSpace SAF Submission Ingestion Package generated from Vireo submission #22919 on 2026-02-19 at 18:25:12"]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Hybrid classical-quantum optimization algorithms in electromagnetic applications"]}]}],"canonical_facts":{"dc:contributor":["Peng, Zhen","Jin, Jian-Ming","Schutt-Aine, Jose","Soltanaghai, Elahé"],"dc:creator":["Lim, Qi Jian"],"dc:date":["2025-12","2025-11-24"],"dc:description":["Physics-based computing based on Ising models has emerged as a powerful paradigm for tackling large-scale combinatorial optimization problems. This dissertation develops a unified Ising-based framework for electromagnetic design, with a particular focus on reconfigurable intelligent surfaces (RISs) and antenna array synthesis. A broad class of electromagnetic tasks—including discrete-phase RIS configuration, antenna beamforming and null steering for multi-user links and wireless power transfer, and planar or conformal array synthesis under quantized amplitude and phase constraints—is reformulated as Ising Hamiltonians. On the algorithmic side, the work investigates both quantum and classical physics-based Ising solvers. Quantum annealing is implemented on D-Wave quantum processing units (QPUs) and used to solve medium-scale instances, and a hybrid classical–quantum divide-and-conquer scheme is developed that decomposes large dense problems into QPU-sized subproblems, which are solved on the annealer and reconciled by classical post-processing. These studies clarify the strengths of quantum annealing and hybrid workflows, as well as current limitations in qubit count and sparse hardware connectivity for very large electromagnetic problems. Complementing the quantum approaches, the dissertation also considers classical physics-inspired Ising machines, in which the dynamics of nonlinear oscillators are emulated on conventional CPUs and GPUs. Among these, I focus on the simulated bifurcation (SB) algorithm, which searches for low-energy Ising states by driving a network of coupled oscillators through a controlled bifurcation process. My core contribution is a detailed nonlinear-dynamics analysis of SB based on an eigenvalue stability framework, which explains how pump schedules govern mode bifurcation and reveals bottlenecks such as retarded bifurcation. Guided by these insights, I introduce time-modulated simulated bifurcation (TM-SB), in which system parameters that are fixed in the classical formulation are deliberately modulated over time, together with a momentum-aware pumping strategy. This time modulation makes the dynamics much more likely to settle into high-quality minima, reducing trapping in shallow local optima and reducing the number of runs needed to obtain good solutions. Numerical studies demonstrate that TM-SB and related Ising-based solvers offer robust performance and strong scalability across these scenarios, often matching or surpassing the best-known classical baselines. Overall, the results position physics-based, Ising-model computing—and TM-SB in particular—as a practical and scalable pathway toward adaptive, energy-efficient smart radio environments and intelligent electromagnetic systems.","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2026-02-19 without embargo terms","The student, Qi Jian Lim, accepted the attached license on 2025-11-21 at 06:28.","The student, Qi Jian Lim, submitted this Dissertation for approval on 2025-11-21 at 07:49.","This Dissertation was approved for publication on 2025-11-24 at 10:17.","DSpace SAF Submission Ingestion Package generated from Vireo submission #22919 on 2026-02-19 at 18:25:12"],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/132520"],"dc:language":["en"],"dc:rights":["© 2025 Qi Jian Lim"],"dc:subject":["Reconfigurable Intelligent Surfaces","Time-Modulated Simulated Bifurcation","Ising-based optimization","Antenna arrays and beamforming","Hybrid classical–quantum optimization"],"dc:title":["Hybrid classical-quantum optimization algorithms in electromagnetic applications"],"dc:type":["text","Thesis"],"thesis:degree_discipline":["Electrical & Computer Engr"],"thesis:degree_level":["Dissertation"],"thesis:degree_name":["Ph.D."],"thesis:institution_name":["University of Illinois Urbana-Champaign"]},"updated_at":"2026-07-22T22:25:07Z"}