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cs.LG2022
TIDE: Time Derivative Diffusion for Deep Learning on Graphs
Maysam Behmanesh, Maximilian Krahn, Maks Ovsjanikov
A prominent paradigm for graph neural networks is based on the message-passing framework. In this framework, information communication is realized only between neighboring nodes. T…
quant-ph2022★ 4 cited
QuAnt: Quantum Annealing with Learnt Couplings
Marcel Seelbach Benkner, Maximilian Krahn, Edith Tretschk +3
Modern quantum annealers can find high-quality solutions to combinatorial optimisation objectives given as quadratic unconstrained binary optimisation (QUBO) problems. Unfortunatel…