Deep Learning of Quantum Many-Body Dynamics via Random Driving. - Naeimeh Mohseni; Thomas Fösel; Lingzhen Guo; Carlos Navarrete-Benlloch; Florian Marquardt (Quantum, 2022)

Deep recurrent networks predicting the gap evolution in adiabatic quantum computing. - Naeimeh Mohseni; Carlos Navarrete-Benlloch; Tim Byrnes; Florian Marquardt (Quantum, 2023)

A Machine Learning Approach to Boost the Vehicle-2-Grid Scheduling. - Gabriele Agliardi; Giorgio Cortiana; Anton Dekusar; Kumar Jang Bahadur Ghosh; Naeimeh Mohseni; Corey O'Meara; Víctor Valls; Kavitha Yogaraj; Sergiy Zhuk (iSPEC, 2024)

Towards Less Greedy Quantum Coalition Structure Generation in Induced Subgraph Games. - Jonas Nüßlein; Daniëlle Schuman; David Bucher; Naeimeh Mohseni; Kumar Jang Bahadur Ghosh; Corey O'Meara; Giorgio Cortiana; Claudia Linnhoff-Popien (QCE, 2024)

Mitigating exponential concentration in covariant quantum kernels for subspace and real-world data. - Gabriele Agliardi; Giorgio Cortiana; Anton Dekusar; Kumar Jang Bahadur Ghosh; Naeimeh Mohseni; Corey O'Meara; Víctor Valls; Kavitha Yogaraj; Sergiy Zhuk (CoRR, 2024)

Deep learning of many-body observables and quantum information scrambling. - Naeimeh Mohseni; Junheng Shi; Tim Byrnes; Michael J. Hartmann (Quantum, 2024)

Q-GRID: Quantum Optimization for the Future Energy Grid. - Jonas Blenninger; David Bucher; Giorgio Cortiana; Kumar Jang Bahadur Ghosh; Naeimeh Mohseni; Jonas Nüßlein; Corey O'Meara; Daniel Porawski; Benedikt Wimmer (Künstliche Intell., 2024)

Evaluating Quantum Optimization for Dynamic Self-Reliant Community Detection. - David Bucher; Daniel Porawski; Benedikt Wimmer; Jonas Nüßlein; Corey O'Meara; Naeimeh Mohseni; Giorgio Cortiana; Claudia Linnhoff-Popien (IEEE Trans. Smart Grid, 2025)