Multi-objective Quantum Optimization based on Bayesian Approach for Computing Resource Assignment Problems

In quantum optimization approaches, such as Quantum Approximate Optimization Algorithms (QAOA) and quantum annealing, the optimal solution is obtained by minimizing an energy function that encapsulates the optimization problem. In the case of multi-objective optimization problems, the trade-offs between competing objective functions are managed by adjusting a set of weight parameters. This paper investigates two parameter-tuning techniques, a grid search and Bayesian optimization, applied to a computational resource allocation problem with multiple objective functions. We compare the performance of these methods by evaluating the efficiency, diversity, and uniformity of the Pareto solutions derived from the optimization results.