2026-09-19T12:38:45Zhttps://iris.unitn.it/oai/request

oai:iris.unitn.it:11572/4526182026-04-03T00:48:28Zcom_11572_237821com_11572_101871col_11572_237822ou_ou00011

Quantum computing for biophysical and optimization problems Panizza, Veronica Panizza, Veronica Hauke, Philipp Hans Juergen Faccioli, Pietro Pastorello, Davide Blanzieri, Enrico quantum computing biophysics protein design lattice gauge theories entanglement 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. 2025-05-07 info:eu-repo/semantics/doctoralThesis https://hdl.handle.net/11572/452618 http://dx.doi.org/10.15168/11572_452618 10.15168/11572_452618 eng firstpage:1 lastpage:185 numberofpages:185 info:eu-repo/semantics/openAccess Università degli studi di Trento place:TRENTO license:Creative commons license uri:http://creativecommons.org/licenses/by/4.0/