1 citations · 1 across the 2 of their papers we have counts for
8 papers
Hybrid Quantum Neural Networks: Theory, Implementations, and Applications
Léo Monbroussou, Maniraman Periyasamy, Viacheslav Kuzmin +4
Artificial intelligence has been transformed by deep neural networks, yet the search for new learning architectures continues. Quantum machine learning offers one such direction, a…
Multi-objective optimization and quantum hybridization of equivariant deep learning interatomic potentials
G. Laskaris, D. Morozov, D. Tarpanov +6
Allegro is a machine learning interatomic potential model designed to predict atomic properties in molecules using E(3) equivariant neural networks. When training this model, there…
Hybrid Fourier Neural Operator for Surrogate Modeling of Laser Processing with a Quantum-Circuit Mixer
Mateusz Papierz, Asel Sagingalieva, Alix Benoit +3
Data-driven surrogates can replace expensive multiphysics solvers for parametric PDEs, yet building compact, accurate neural operators for three-dimensional problems remains challe…
A Fast and Generalizable Fourier Neural Operator-Based Surrogate for Melt-Pool Prediction in Laser Processing
Alix Benoit, Toni Ivas, Mateusz Papierz +3
High-fidelity simulations of laser welding capture complex thermo-fluid phenomena, including phase change, free-surface deformation, and keyhole dynamics, however their computation…
Predictive control of blast furnace temperature in steelmaking with hybrid depth-infused quantum neural networks
Nayoung Lee, Minsoo Shin, Asel Sagingalieva +5
Accurate prediction and stabilization of blast furnace temperatures are crucial for optimizing the efficiency and productivity of steel production. Traditional methods often strugg…
Multi-stream physics hybrid networks for solving Navier-Stokes equations
Aleksandr Sedykh, Tatjana Protasevich, Mikhail Surmach +4
Understanding and solving fluid dynamics equations efficiently remains a fundamental challenge in computational physics. Traditional numerical solvers and physics-informed neural n…