  Quantum AI is an emerging field that uses quantum computing to solve typical
complex problems in AI. In this work, we propose BILP-Q, the first-ever general
quantum approach for solving the Coalition Structure Generation problem (CSGP),
which is notably NP-hard. In particular, we reformulate the CSGP in terms of a
Quadratic Binary Combinatorial Optimization (QUBO) problem to leverage existing
quantum algorithms (e.g., QAOA) to obtain the best coalition structure. Thus,
we perform a comparative analysis in terms of time complexity between the
proposed quantum approach and the most popular classical baselines.
Furthermore, we consider standard benchmark distributions for coalition values
to test the BILP-Q on small-scale experiments using the IBM Qiskit environment.
Finally, since QUBO problems can be solved operating with quantum annealing, we
run BILP-Q on medium-size problems using a real quantum annealer (D-Wave).
