{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/151228"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/151228","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"Artificial Neural Networks for Programming Quantum Annealers","abstract":"Quantum machine learning is an emerging field of research at the intersection of quantum computing and machine learning. It has the potential to enable advances in artificial intelligence, such as solving problems intractable on classical computers. Some of the fundamental ideas behind quantum machine learning are very similar to kernel methods in classical machine learning. Both process information by mapping it into high-dimensional vector spaces without explicitly calculating their numerical values. Quantum annealers are mostly studied in the adiabatic regime, a computational model in which the quantum system remains in an instantaneous ground energy eigenstate of a time-dependent Hamiltonian. Our research focuses on the diabatic regime where the quantum state does not necessarily remain in the ground state during computation. Concretely, we explore a setup for performing classification on labeled classical datasets, consisting of a classical neural network connected to a quantum annealer. The neural network programs the quantum annealer's controls and thereby maps the annealer's initial states into new states in the Hilbert space. The neural network's parameters are optimized in a way that maximizes the distance of states corresponding to inputs from different classes and minimizes the distance between quantum states corresponding to the same class. Recent literature showed that at least some of the \"learning\" is due to the quantum annealer, connecting a small linear network to a quantum annealer and using it to learn small and linearly inseparable datasets. In this study, we simulate this system to learn several common datasets, including those for image and sound recognition. We conclude that adding a small quantum annealer does not provide a significant benefit over just using a regular (nonlinear) classical neural network.","abstract_html":"Quantum machine learning is an emerging field of research at the intersection of quantum computing and machine learning. It has the potential to enable advances in artificial intelligence, such as solving problems intractable on classical computers. Some of the fundamental ideas behind quantum machine learning are very similar to kernel methods in classical machine learning. Both process information by mapping it into high-dimensional vector spaces without explicitly calculating their numerical values. Quantum annealers are mostly studied in the adiabatic regime, a computational model in which the quantum system remains in an instantaneous ground energy eigenstate of a time-dependent Hamiltonian. Our research focuses on the diabatic regime where the quantum state does not necessarily remain in the ground state during computation. Concretely, we explore a setup for performing classification on labeled classical datasets, consisting of a classical neural network connected to a quantum annealer. The neural network programs the quantum annealer's controls and thereby maps the annealer's initial states into new states in the Hilbert space. The neural network's parameters are optimized in a way that maximizes the distance of states corresponding to inputs from different classes and minimizes the distance between quantum states corresponding to the same class. Recent literature showed that at least some of the "learning" is due to the quantum annealer, connecting a small linear network to a quantum annealer and using it to learn small and linearly inseparable datasets. In this study, we simulate this system to learn several common datasets, including those for image and sound recognition. We conclude that adding a small quantum annealer does not provide a significant benefit over just using a regular (nonlinear) classical neural network.","abstract_has_math":false,"creators":["Bosch, Samuel"],"institution":"Massachusetts Institute of Technology","degree_name":"Master","degree_level":null,"degree_discipline":null,"degree_department":"Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science","school":null,"contributors":[],"advisors":["Lloyd, Seth"],"committee_chairs":[],"committee_members":[],"year":2023,"date_issued":"2023-06","date_published":"2023-06","updated_at":"2026-07-22T22:21:02Z","subjects":[],"languages":[],"rights":["In Copyright - Educational Use Permitted","Copyright retained by author(s)"],"rights_urls":["https://rightsstatements.org/page/InC-EDU/1.0/"],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/1721.1/151228","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Lloyd, Seth"]},{"key":"dc:contributor.department","label":"Department","values":["Massachusetts Institute of Technology. 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It has the potential to enable advances in artificial intelligence, such as solving problems intractable on classical computers. Some of the fundamental ideas behind quantum machine learning are very similar to kernel methods in classical machine learning. Both process information by mapping it into high-dimensional vector spaces without explicitly calculating their numerical values. Quantum annealers are mostly studied in the adiabatic regime, a computational model in which the quantum system remains in an instantaneous ground energy eigenstate of a time-dependent Hamiltonian. Our research focuses on the diabatic regime where the quantum state does not necessarily remain in the ground state during computation. Concretely, we explore a setup for performing classification on labeled classical datasets, consisting of a classical neural network connected to a quantum annealer. The neural network programs the quantum annealer's controls and thereby maps the annealer's initial states into new states in the Hilbert space. The neural network's parameters are optimized in a way that maximizes the distance of states corresponding to inputs from different classes and minimizes the distance between quantum states corresponding to the same class. Recent literature showed that at least some of the \"learning\" is due to the quantum annealer, connecting a small linear network to a quantum annealer and using it to learn small and linearly inseparable datasets. In this study, we simulate this system to learn several common datasets, including those for image and sound recognition. We conclude that adding a small quantum annealer does not provide a significant benefit over just using a regular (nonlinear) classical neural network."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["S.M."]},{"key":"dc:title","label":"Title","values":["Artificial Neural Networks for Programming Quantum Annealers"]}]}],"canonical_facts":{"dc:contributor.advisor":["Lloyd, Seth"],"dc:contributor.department":["Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science"],"dc:creator":["Bosch, Samuel"],"dc:date.accessioned":["2023-07-31T19:24:21Z"],"dc:date.available":["2023-07-31T19:24:21Z"],"dc:date.issued":["2023-06"],"dc:description.abstract":["Quantum machine learning is an emerging field of research at the intersection of quantum computing and machine learning. It has the potential to enable advances in artificial intelligence, such as solving problems intractable on classical computers. Some of the fundamental ideas behind quantum machine learning are very similar to kernel methods in classical machine learning. Both process information by mapping it into high-dimensional vector spaces without explicitly calculating their numerical values. Quantum annealers are mostly studied in the adiabatic regime, a computational model in which the quantum system remains in an instantaneous ground energy eigenstate of a time-dependent Hamiltonian. Our research focuses on the diabatic regime where the quantum state does not necessarily remain in the ground state during computation. Concretely, we explore a setup for performing classification on labeled classical datasets, consisting of a classical neural network connected to a quantum annealer. The neural network programs the quantum annealer's controls and thereby maps the annealer's initial states into new states in the Hilbert space. The neural network's parameters are optimized in a way that maximizes the distance of states corresponding to inputs from different classes and minimizes the distance between quantum states corresponding to the same class. Recent literature showed that at least some of the \"learning\" is due to the quantum annealer, connecting a small linear network to a quantum annealer and using it to learn small and linearly inseparable datasets. In this study, we simulate this system to learn several common datasets, including those for image and sound recognition. We conclude that adding a small quantum annealer does not provide a significant benefit over just using a regular (nonlinear) classical neural network."],"dc:description.degree":["S.M."],"dc:identifier.uri":["https://hdl.handle.net/1721.1/151228"],"dc:publisher":["Massachusetts Institute of Technology"],"dc:rights":["In Copyright - Educational Use Permitted","Copyright retained by author(s)"],"dc:rights.uri":["https://rightsstatements.org/page/InC-EDU/1.0/"],"dc:title":["Artificial Neural Networks for Programming Quantum Annealers"],"dc:type":["Thesis"],"thesis:degree_name":["Master","Master of Science in Electrical Engineering and Computer Science"]},"updated_at":"2026-07-22T22:21:02Z"}