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Found 7 papers, 3 papers with code
Training Neural Networks with Universal Adiabatic Quantum Computing
The training of neural networks (NNs) is a computationally intensive task requiring significant time and resources.
Identifying magnetic antiskyrmions while they form with convolutional neural networks
We use a Convolutional Neural Network (CNN) to identify the relevant features in the thermodynamical phases of chiral magnets, including (anti-)skyrmions, bimerons, and helical and ferromagnetic states.
Qade: Solving Differential Equations on Quantum Annealers
We present a general method, called Qade, for solving differential equations using a quantum annealer.
Elvet -- a neural network-based differential equation and variational problem solver
We present Elvet, a Python package for solving differential equations and variational problems using machine learning methods.
Higher-spin particles at high-energy colliders
Using an effective field theory approach for higher-spin fields, we derive the interactions of colour singlet and electrically neutral particles with a spin higher than unity, concentrating on the spin-3/2, spin-2, spin-5/2 and spin-3 cases.
High Energy Physics - Phenomenology High Energy Physics - Experiment High Energy Physics - Theory Nuclear Theory
The Emergence of Electroweak Skyrmions through Higgs Bosons
Skyrmions are extended field configurations, initially proposed to describe baryons as topological solitons in an effective field theory of mesons.
High Energy Physics - Phenomenology High Energy Physics - Experiment High Energy Physics - Theory
BasisGen: automatic generation of operator bases
BasisGen is a Python package for the automatic generation of bases of operators in effective field theories.
High Energy Physics - Phenomenology
