no code implementations • 10 Apr 2023 • Yanming Che, Clemens Gneiting, Franco Nori
For a family of Hamiltonians defined on an $m$-dimensional space of physical parameters, the ground state and its properties at an arbitrary parameter configuration can be predicted via a machine learning protocol up to a prescribed prediction error $\varepsilon$, provided that a sample set (of size $N$) of the states can be efficiently prepared and measured.
no code implementations • 6 Feb 2022 • Yanming Che, Clemens Gneiting, Franco Nori
Feynman path integrals provide an elegant, classically inspired representation for the quantum propagator and the quantum dynamics, through summing over a huge manifold of all possible paths.
