4 papers
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…
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…
Efficient Operator Selection and Warm-Start Strategy for Excitations in Variational Quantum Eigensolvers
Max Haas, Thierry N. Kaldenbach, Thomas Hammerschmidt +1
We present a novel approach for efficient preparation of electronic ground states, leveraging the optimizer ExcitationSolve [Jäger et al., Comm. Phys. (2025)] and established varia…
A Joint Quantum Computing, Neural Network and Embedding Theory Approach for the Derivation of the Universal Functional
Martin J. Uttendorfer, Daniel Barragan-Yani, Matthias Sperl +1
We introduce a novel approach that exploits the intersection of quantum computing, machine learning and reduced density matrix functional theory to leverage the potential of quantu…