  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 works. 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.
