79 citations · 175 across the 5 of their papers we have counts for
5 papers
Zero Variance Markov Chain Monte Carlo for Bayesian Estimators
Antonietta Mira, Reza Solgi, Daniele Imparato
Interest is in evaluating, by Markov chain Monte Carlo (MCMC) simulation, the expected value of a function with respect to a, possibly unnormalized, probability distribution. A gen…
A Robertson-type Uncertainty Principle and Quantum Fisher Information
Paolo Gibilisco, Daniele Imparato, Tommaso Isola
Let be complex selfadjoint matrices and let be a density matrix. The Robertson uncertainty principle $$ det (Cov_ρ(A_h,A_j)) \geq det (- \frac{i}{2} Tr (ρ[A_h,A_j…
A volume inequality for quantum Fisher information and the uncertainty principle
P. Gibilisco, D. Imparato, T. Isola
Let be complex self-adjoint matrices and let be a density matrix. The Robertson uncertainty principle $$ det(Cov_ρ(A_h,A_j)) \geq det(- \frac{i}{2} Tr(ρ[A_h,A_j])…
Inequalities for quantum Fisher information
Paolo Gibilisco, Daniele Imparato, Tommaso Isola
In 2003 Luo proved an inequality relating the Wigner-Yanase information and the -information. In this paper we prove that Luo's inequality is a particular case of a general in…
Uncertainty Principle and Quantum Fisher Information - II
P. Gibilisco, D. Imparato, T. Isola
Heisenberg and Schr{ö}dinger uncertainty principles give lower bounds for the product of variances , in a state , if the observables are not compat…