Binary Optimization with Complex Constraints via Quantum Approximate Multi-Objective Optimization
This work reformulates binary optimization problems with complex constraints into a Multi-Objective Quadratic Unconstrained Binary Optimization (MO-QUBO) problem. It uses Quantum Approximate Multi-Objective Optimization (QAMOO) to find candidate solutions approximating the Pareto front. This allows for classical evaluation of non-quadratic constraints, such as Conditional Value-at-Risk (CVaR), leading to a feasible portfolio selection.