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>