Mobility · automotive
A multi-objective search over (Al,Ga,In)₂O₃ compositions on the public NOMAD2018 dataset: in a 60-second bandgap run the quantum-inspired search scored 123.5 against 100.5 for NSGA-II and 76.2 for TPE, and reached NSGA-II’s 60-second score in about 10 seconds. A computational preprint delivered through our platform network.
Headline result
scores 123.5 against 100.5 and 76.2; one public dataset; a preprint
Platform networkClassical computation
Finding compositions that balance conductivity and transparency means searching a large composition space where each evaluation costs a simulation. Evolutionary and Bayesian searches are the standard, and they are slow to reach a good set.
A factorization-machine model of the composition space was searched with a quantum-inspired annealer on classical hardware and compared against NSGA-II and TPE at equal wall-clock time on the same dataset.
scores 123.5 against 100.5 and 76.2; one public dataset; a preprint
For a client, the next step is the first engagement on this page’s industry: you send one controller and its operating point. You get back the safe-episode comparison against your current model. Two weeks.
Decision it enabled
A better candidate set in the same minute
Instead of: NSGA-II and TPE at the same 60 seconds
Verified by · Delivered through our platform network; quantum-inspired, classical hardware; computational preprint; one dataset.
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