Bayesian predictive density operators for exchangeable quantum-statistical models

Quantum state estimation has been widely investigated and there are mainly two approaches proposed: One is based on the point estimation of an unknown parameter and the other is based on the Bayesian method. We adopt the relative entropy from the true state to a predictive density operator as a loss function. We consider exchangeable quantum models with an arbitrary chosen measurement and show that Bayesian predictive density operators are the best predictive density operators when we evaluate them by using the average relative entropy based on a prior. This result is a quantum version of Aitchison's result in classical statistics.