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cond-mat.mtrl-sci2026
QUBO-Compatible Active Learning for Inverse Design of High-Entropy Alloys
Giorgio Silvi, Kirsten Bark, Rolando Reiner +5
Machine-learned forward models can rapidly predict alloy properties, but their use for inverse design remains challenging when the search should also retain compatibility with quad…
cond-mat.mtrl-sci2026
Data-driven multi-objective optimization for alloy recycling using factorization machines and quantum annealing
Thomas Plehn, Katrin Bugelnig, Silvana Tumminello +2
Quantum annealing has the potential to provide practical quantum advantage for complex optimization tasks. Here, we present a systematic assessment of an integrated factorization-m…
cond-mat.mtrl-sci2025
Progress on Data-Driven, Multi-Objective Quantum Optimization
Thomas Plehn, Daniel Barragan-Yani, Eric Breitbarth +2
Here, we present two complementary approaches that advance quadratic unconstrained binary optimization (QUBO) toward practical use in data-driven materials design and other real-va…