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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Bibliographic Details
Published in:2024 IEEE International Conference on Quantum Computing and Engineering (QCE) Vol. 1; pp. 1617 - 1627
Main Authors: Kruse, Georg, Coelho, Rodrigo, Rosskopf, Andreas, Wille, Robert, Lorenz, Jeanette Miriam
Format: Conference Proceeding
Language:English
Published: IEEE 15.09.2024
Subjects:
Combinatorial Optimization
Graph neural networks
Hands
Hardware
Neural Combinatorial Optimization
Optimization
Quantum algorithm
Quantum Reinforcement Learning
Reinforcement learning
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Summary: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.
DOI:10.1109/QCE60285.2024.00189