FM+QA with data-driven Tchebycheff scalarization finds non-convex Pareto alloy mixtures on D-Wave hardware and matches classical FM+SA up to ~25 binary qubits, while one-hot encoding degrades earlier.
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Data-driven multi-objective optimization for alloy recycling using factorization machines and quantum annealing
FM+QA with data-driven Tchebycheff scalarization finds non-convex Pareto alloy mixtures on D-Wave hardware and matches classical FM+SA up to ~25 binary qubits, while one-hot encoding degrades earlier.