Evaluating Quantum Annealing for Grouping Optimization with all Members' Compatibility.

Quantum annealing is a novel computing approach expected to rapidly solve combinatorial optimization problems. Grouping optimization problem is one of the combinatorial optimization problems that maximize group effectiveness by composing groups based on member's attributes, personality, and compatibility. There is no model for the grouping problem where the group effectiveness is calculated by compatibility of three or more members. In this paper, we formulate a general model of the grouping optimization for quantum annealing, and evaluate it assuming collaborative learning on quantum annealing machine in comparison with simulated annealing and hill climbing.