10.6084/m9.figshare.6197069.v1
Kyle Cranmer
Kyle
Cranmer
Duccio Pappadopulo
Duccio
Pappadopulo
Siavash Golkar
Siavash
Golkar
Quantum Inference and Quantum Flows
figshare
2018
machine learning
density matrix
flows
quantum mechanics
Condensed Matter Physics
Computational Physics
Knowledge Representation and Machine Learning
Applied Statistics
2018-04-27 19:46:17
Journal contribution
https://figshare.com/articles/journal_contribution/Quantum_Inference_and_Quantum_Flows/6197069
<div>We have introduced a method to extract energy eigenvalues and eigenstates from lattice simulations</div><div>• essentially maximum likelihood for density matrices</div><div>• can extract several excited states accurately</div><div>• demonstrated with SHO, but technique applies to more complicated classical action</div><div><br></div><div>We have also introduced Quantum Flows</div><div>• unitary operators via neural network-based bijections</div>