Data-driven multi-objective optimization for alloy recycling using factorization machines and quantum annealing

Abstract

Quantum annealing has the potential to provide practical quantum advantage for complex optimization tasks. Here, we present a systematic assessment of an integrated factorization-machine and quantum-annealing workflow (FM+QA) for a technologically relevant application: multi-objective Pareto optimization in metal up-cycling through alloy design. To address the non-convex nature of the Pareto front, we employ the recently proposed data-driven Tchebycheff scalarization (DDTS) scheme. Our results show that FM+QA extends the applicability of QUBO-based optimization to data-driven materials discovery problems with multiple competing objectives. In particular, we analyze the scaling behavior of the approach and compare quantum annealing with classical simulated annealing using both regular binary encoding and one-hot encoding. Finally, we provide a critical perspective on the problem sizes and encoding strategies for which quantum-annealing-based optimization may become practically beneficial in the near future.