A drift functions is learned from samples created by the half-bridge of the previous iteration using either a Gaussian process regression approach or by training a deep neural network. The estimation of the required (backward) drift functions from samples of the forward processes (and vice versa)...
This provides a bridge between the quantum uncertainty principle and nonequilibrium quantum thermodynamic relations. It may not be too unexpected that the effective temperature T eff − 1 ( t ) = β eff − 1 ( t ) is independent of the squeeze parameter ζ , even though squeezing accounts...