Quantum-Inference-Flatiron-CCQ.pdf (23.83 MB)

Quantum Inference and Quantum Flows

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journal contribution
posted on 27.04.2018 by Kyle Cranmer, Duccio Pappadopulo, Siavash Golkar
We have introduced a method to extract energy eigenvalues and eigenstates from lattice simulations
• essentially maximum likelihood for density matrices
• can extract several excited states accurately
• demonstrated with SHO, but technique applies to more complicated classical action

We have also introduced Quantum Flows
• unitary operators via neural network-based bijections


National Science Foundation, Moore Sloan Data Science Environment at NYU