{"id":{"repo_id":"trento","oai_identifier":"oai:iris.unitn.it:11572/452618"},"canonical_url":"https://search.dev.ndltd.org/etd/trento/oai:iris.unitn.it:11572/452618","repository":{"repo_id":"trento","name":"Università degli Studi di Trento","base_url":"https://iris.unitn.it/oai/request"},"display":{"title":"Quantum computing for biophysical and optimization problems","abstract":"The remarkable progress of quantum technologies over recent years has driven significant efforts toward developing algorithms with applications to a wide range of research fields. Beyond fully quantum algorithms — whose efficacy remains constrained by technological limitations — hybrid quantum-classical algorithms and quantum-inspired methods have emerged as promising avenues for tackling real-world problems. In this study, we focus on two particularly challenging biophysics problems: protein design and polymer sampling. Protein design involves engineering the primary sequence of a protein to ensure that it folds into a specific target conformation of biological interest. Our approach employs a physics-based machine learning model that incorporates a QUBO (Quadratic Unconstrained Binary Optimization) encoding of the design problem that is amenable to adiabatic quantum platforms such as the D-Wave device. For the polymer sampling problem — where the objective is to sample both the sequence and the conformation of polymers according to a thermal distribution — we establish a deep connection with an Abelian lattice gauge theory populated with fermions. Building on this theoretical framework, we develop a quantum-inspired Monte Carlo protocol that not only eliminates the sign problem but also features a decorrelation time that scales linearly with the system size in the dense-melt polymer regime, providing a novel approach to computational polymer physics. Within the framework of lattice gauge theories, where physically realizable measurements are heavily constrained by local symmetries, we analyze from the quantum-information perspective the problem of pinpointing entangled states by resorting to entanglement witnesses. Furthermore, we develop a numerical optimization protocol that enhances the effectiveness of entanglement witnesses while ensuring their physical implementation within the lattice gauge theory framework.","abstract_html":"The remarkable progress of quantum technologies over recent years has driven significant efforts toward developing algorithms with applications to a wide range of research fields. Beyond fully quantum algorithms — whose efficacy remains constrained by technological limitations — hybrid quantum-classical algorithms and quantum-inspired methods have emerged as promising avenues for tackling real-world problems. In this study, we focus on two particularly challenging biophysics problems: protein design and polymer sampling. Protein design involves engineering the primary sequence of a protein to ensure that it folds into a specific target conformation of biological interest. Our approach employs a physics-based machine learning model that incorporates a QUBO (Quadratic Unconstrained Binary Optimization) encoding of the design problem that is amenable to adiabatic quantum platforms such as the D-Wave device. For the polymer sampling problem — where the objective is to sample both the sequence and the conformation of polymers according to a thermal distribution — we establish a deep connection with an Abelian lattice gauge theory populated with fermions. Building on this theoretical framework, we develop a quantum-inspired Monte Carlo protocol that not only eliminates the sign problem but also features a decorrelation time that scales linearly with the system size in the dense-melt polymer regime, providing a novel approach to computational polymer physics. Within the framework of lattice gauge theories, where physically realizable measurements are heavily constrained by local symmetries, we analyze from the quantum-information perspective the problem of pinpointing entangled states by resorting to entanglement witnesses. Furthermore, we develop a numerical optimization protocol that enhances the effectiveness of entanglement witnesses while ensuring their physical implementation within the lattice gauge theory framework.","abstract_has_math":false,"creators":["Panizza, Veronica"],"institution":"Università degli studi di Trento","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":["Hauke, Philipp Hans Juergen","Faccioli, Pietro","Pastorello, Davide","Blanzieri, Enrico"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2025,"date_issued":"2025-05-07","date_published":"2025-05-07","updated_at":"2026-07-24T05:04:22Z","subjects":["quantum computing","biophysics","protein design","lattice gauge theories","entanglement"],"languages":["eng"],"rights":["info:eu-repo/semantics/openAccess","license:Creative commons","license uri:http://creativecommons.org/licenses/by/4.0/"],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier","label":"Identifier","values":["http://dx.doi.org/10.15168/11572_452618","10.15168/11572_452618"],"render_values":[{"text":"http://dx.doi.org/10.15168/11572_452618","href":"http://dx.doi.org/10.15168/11572_452618","code":true},{"text":"10.15168/11572_452618","href":"https://doi.org/10.15168/11572_452618","code":true}]}]},"links":{"outbound_url":"https://hdl.handle.net/11572/452618","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Panizza, Veronica","Hauke, Philipp Hans Juergen","Faccioli, Pietro","Pastorello, Davide","Blanzieri, Enrico"]},{"key":"dc:creator","label":"Author","values":["Panizza, Veronica"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2025-05-07"]},{"key":"dc:publisher","label":"Institution","values":["Università degli studi di Trento","place:TRENTO"]},{"key":"dc:relation","label":"Dc Relation","values":["firstpage:1","lastpage:185","numberofpages:185"]},{"key":"dc:type","label":"Dc Type","values":["info:eu-repo/semantics/doctoralThesis"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["quantum computing","biophysics","protein design","lattice gauge theories","entanglement"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["eng"]},{"key":"dc:rights","label":"Dc Rights","values":["info:eu-repo/semantics/openAccess","license:Creative commons","license uri:http://creativecommons.org/licenses/by/4.0/"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/11572/452618","http://dx.doi.org/10.15168/11572_452618","10.15168/11572_452618"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["The remarkable progress of quantum technologies over recent years has driven significant efforts toward developing algorithms with applications to a wide range of research fields. Beyond fully quantum algorithms — whose efficacy remains constrained by technological limitations — hybrid quantum-classical algorithms and quantum-inspired methods have emerged as promising avenues for tackling real-world problems. In this study, we focus on two particularly challenging biophysics problems: protein design and polymer sampling. Protein design involves engineering the primary sequence of a protein to ensure that it folds into a specific target conformation of biological interest. Our approach employs a physics-based machine learning model that incorporates a QUBO (Quadratic Unconstrained Binary Optimization) encoding of the design problem that is amenable to adiabatic quantum platforms such as the D-Wave device. For the polymer sampling problem — where the objective is to sample both the sequence and the conformation of polymers according to a thermal distribution — we establish a deep connection with an Abelian lattice gauge theory populated with fermions. Building on this theoretical framework, we develop a quantum-inspired Monte Carlo protocol that not only eliminates the sign problem but also features a decorrelation time that scales linearly with the system size in the dense-melt polymer regime, providing a novel approach to computational polymer physics. Within the framework of lattice gauge theories, where physically realizable measurements are heavily constrained by local symmetries, we analyze from the quantum-information perspective the problem of pinpointing entangled states by resorting to entanglement witnesses. Furthermore, we develop a numerical optimization protocol that enhances the effectiveness of entanglement witnesses while ensuring their physical implementation within the lattice gauge theory framework."]},{"key":"dc:title","label":"Title","values":["Quantum computing for biophysical and optimization problems"]}]}],"canonical_facts":{"dc:contributor":["Panizza, Veronica","Hauke, Philipp Hans Juergen","Faccioli, Pietro","Pastorello, Davide","Blanzieri, Enrico"],"dc:creator":["Panizza, Veronica"],"dc:date":["2025-05-07"],"dc:description":["The remarkable progress of quantum technologies over recent years has driven significant efforts toward developing algorithms with applications to a wide range of research fields. Beyond fully quantum algorithms — whose efficacy remains constrained by technological limitations — hybrid quantum-classical algorithms and quantum-inspired methods have emerged as promising avenues for tackling real-world problems. In this study, we focus on two particularly challenging biophysics problems: protein design and polymer sampling. Protein design involves engineering the primary sequence of a protein to ensure that it folds into a specific target conformation of biological interest. Our approach employs a physics-based machine learning model that incorporates a QUBO (Quadratic Unconstrained Binary Optimization) encoding of the design problem that is amenable to adiabatic quantum platforms such as the D-Wave device. For the polymer sampling problem — where the objective is to sample both the sequence and the conformation of polymers according to a thermal distribution — we establish a deep connection with an Abelian lattice gauge theory populated with fermions. Building on this theoretical framework, we develop a quantum-inspired Monte Carlo protocol that not only eliminates the sign problem but also features a decorrelation time that scales linearly with the system size in the dense-melt polymer regime, providing a novel approach to computational polymer physics. Within the framework of lattice gauge theories, where physically realizable measurements are heavily constrained by local symmetries, we analyze from the quantum-information perspective the problem of pinpointing entangled states by resorting to entanglement witnesses. Furthermore, we develop a numerical optimization protocol that enhances the effectiveness of entanglement witnesses while ensuring their physical implementation within the lattice gauge theory framework."],"dc:identifier":["https://hdl.handle.net/11572/452618","http://dx.doi.org/10.15168/11572_452618","10.15168/11572_452618"],"dc:language":["eng"],"dc:publisher":["Università degli studi di Trento","place:TRENTO"],"dc:relation":["firstpage:1","lastpage:185","numberofpages:185"],"dc:rights":["info:eu-repo/semantics/openAccess","license:Creative commons","license uri:http://creativecommons.org/licenses/by/4.0/"],"dc:subject":["quantum computing","biophysics","protein design","lattice gauge theories","entanglement"],"dc:title":["Quantum computing for biophysical and optimization problems"],"dc:type":["info:eu-repo/semantics/doctoralThesis"]},"updated_at":"2026-07-24T05:04:22Z"}