Hamiltonian-Based Quantum Reinforcement Learning for Neural Combinatorial Optimization
Advancements in Quantum Computing (QC) and Neural Combinatorial Optimization (NCO) represent promising steps in tackling complex computational challenges. On the one hand, Variational Quantum Algorithms such as QAOA can be used to solve a wide range of combinatorial optimization problems. On the oth...
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| Vydáno v: | 2024 IEEE International Conference on Quantum Computing and Engineering (QCE) Ročník 1; s. 1617 - 1627 |
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15.09.2024
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| Abstract | Advancements in Quantum Computing (QC) and Neural Combinatorial Optimization (NCO) represent promising steps in tackling complex computational challenges. On the one hand, Variational Quantum Algorithms such as QAOA can be used to solve a wide range of combinatorial optimization problems. On the other hand, the same class of problems can be solved by NCO, a method that has shown promising results, particularly since the introduction of Graph Neural Networks. Given recent advances in both research areas, we introduce Hamiltonian-based Quantum Reinforcement Learning (QRL), an approach at the intersection of QC and NCO. We model our ansatzes directly on the combinatorial optimization problem's Hamiltonian formulation, which allows us to apply our approach to a broad class of problems. Our ansatzes show favourable trainability properties when compared to the hardware efficient ansatzes, while also not being limited to graph-based problems, unlike previous approaches. In this work, we evaluate the performance of Hamiltonian-based QRL on a diverse set of combinatorial optimization problems to demonstrate the broad applicability of our approach and compare it to QAOA. |
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| AbstractList | Advancements in Quantum Computing (QC) and Neural Combinatorial Optimization (NCO) represent promising steps in tackling complex computational challenges. On the one hand, Variational Quantum Algorithms such as QAOA can be used to solve a wide range of combinatorial optimization problems. On the other hand, the same class of problems can be solved by NCO, a method that has shown promising results, particularly since the introduction of Graph Neural Networks. Given recent advances in both research areas, we introduce Hamiltonian-based Quantum Reinforcement Learning (QRL), an approach at the intersection of QC and NCO. We model our ansatzes directly on the combinatorial optimization problem's Hamiltonian formulation, which allows us to apply our approach to a broad class of problems. Our ansatzes show favourable trainability properties when compared to the hardware efficient ansatzes, while also not being limited to graph-based problems, unlike previous approaches. In this work, we evaluate the performance of Hamiltonian-based QRL on a diverse set of combinatorial optimization problems to demonstrate the broad applicability of our approach and compare it to QAOA. |
| Author | Lorenz, Jeanette Miriam Coelho, Rodrigo Wille, Robert Rosskopf, Andreas Kruse, Georg |
| Author_xml | – sequence: 1 givenname: Georg surname: Kruse fullname: Kruse, Georg email: georg.kruse@iisb.fraunhofer.de organization: Fraunhofer IISB,Erlangen,Germany – sequence: 2 givenname: Rodrigo surname: Coelho fullname: Coelho, Rodrigo email: rodrigo.coelho@iisb.fraunhofer.de organization: Fraunhofer IISB,Erlangen,Germany – sequence: 3 givenname: Andreas surname: Rosskopf fullname: Rosskopf, Andreas email: andreas.rosskopf@iisb.fraunhofer.de organization: Fraunhofer IISB,Erlangen,Germany – sequence: 4 givenname: Robert surname: Wille fullname: Wille, Robert email: robert.wille@tum.de organization: Technical University Munich,Munich,Germany – sequence: 5 givenname: Jeanette Miriam surname: Lorenz fullname: Lorenz, Jeanette Miriam email: jeanette.miriam.lorenz@iks.fraunhofer.de organization: Fraunhofer IKS, Ludwig Maximilian University,Munich,Germany |
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| SubjectTerms | Combinatorial Optimization Graph neural networks Hands Hardware Neural Combinatorial Optimization Optimization Quantum algorithm Quantum Reinforcement Learning Reinforcement learning |
| Title | Hamiltonian-Based Quantum Reinforcement Learning for Neural Combinatorial Optimization |
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