  Quadratic Unconstrained Binary Optimization (QUBO) is a generic technique to
model various NP-hard Combinatorial Optimization problems (CO) in the form of
binary variables. Ising Hamiltonian is used to model the energy function of a
system. QUBO to Ising Hamiltonian is regarded as a technique to solve various
canonical optimization problems through quantum optimization algorithms.
Recently, PI-GNN, a generic framework, has been proposed to address CO problems
over graphs based on Graph Neural Network (GNN) architecture. They introduced a
generic QUBO-formulated Hamiltonian-inspired loss function that was directly
optimized using GNN. PI-GNN is highly scalable but there lies a noticeable
decrease in the number of satisfied constraints when compared to
problem-specific algorithms and becomes more pronounced with increased graph
densities. Here, We identify a behavioral pattern related to it and devise
strategies to improve its performance. Another group of literature uses
Reinforcement learning (RL) to solve the aforementioned NP-hard problems using
problem-specific reward functions. In this work, we also focus on creating a
bridge between the RL-based solutions and the QUBO-formulated Hamiltonian. We
formulate and empirically evaluate the compatibility of the QUBO-formulated
Hamiltonian as the generic reward function in the RL-based paradigm in the form
of rewards. Furthermore, we also introduce a novel Monty Carlo Tree
Search-based strategy with GNN where we apply a guided search through manual
perturbation of node labels during training. We empirically evaluated our
methods and observed up to 44% improvement in the number of constraint
violations compared to the PI-GNN.
