Quantum state estimation with informationally overcomplete measurements

We study informationally overcomplete measurements for quantum state estimation so as to clarify their tomographic significance as compared with minimal informationally complete measurements. We show that informationally overcomplete measurements can improve the tomographic efficiency significantly over minimal measurements when the states of interest have high purities. Nevertheless, the efficiency is still too limited to be satisfactory with respect to figures of merit based on monotone Riemannian metrics, such as the Bures metric and quantum Chernoff metric. In this way, we also pinpoint the limitation of nonadaptive measurements and motivate the study of more sophisticated measurement schemes. In the course of our study, we introduce the best linear unbiased estimator and show that it is equally efficient as the maximum likelihood estimator in the large-sample limit. This estimator may significantly outperform the canonical linear estimator for states with high purities. It is expected to play an important role in experimental designs and adaptive quantum state tomography besides its significance to the current study.