Fig. 11: Bayesian optimization with bad parameters.
From: Variational quantum algorithm for enhanced continuous variable optical phase sensing
Demonstration of the Bayesian optimization over 50 epochs for (a) very high and (b) very low mean and variance of the hyper-parameters of the Gaussian Process. (Top) The phase angles set by the algorithm. (Middle) The measured cost function C = 1/F. The dotted line is the shot noise limit taking into account the number of photons in the measurement and the number of samples used to estimate the cost function. (Bottom) The measured quadrature mean values and variances. The dotted lines are the optimal values predicted by the theory (“section IIIB”).
