State estimation of N-level quantum systems

Chuanwei Zhang, Chuan-Feng Li, Guang-Can Guo

F. De Martini, AND SCIARRINO F., MARCO RICCI

Yeong Deok Han, Joonwoo Bae, Xiang‐Bin Wang +1 more

We obtain a simple derivation of the optimal quantum state estimation of a two-level system using the no-signaling principle. In particular, we show that the no-signaling principle determines the unique form of the guessing probability, independently to a given figure of merit such as the fidelity or the information gain. This proves that optimal measurements for a two-level quantum system is the same for almost all figures of merit.

R. Derka, Vladimír Bužek, ARTUR K. EKERT

We present a universal algorithm for the optimal quantum state estimation of an arbitrary finite dimensional system. The algorithm specifies a physically realizable (i.e., finite) positive operator valued measurement on a finite number of identically prepared systems. We illustrate the general formalism by applying it to different scenarios of the state estimation of $N$ independent and identically prepared two-level systems (qubits).

László Ruppert

Nattima Thong-O, T. Angsachon, Atirach Ritboon

Quantum state estimation is one of the most extensively studied topics in the field of quantum theory and quantum information. This process is essential in quantum protocols to ensure that the estimated state matches the true state. Typically, the quantum state of identical systems can be estimated from the statistics of measurement outcomes across different measurement bases. In this research, we study adaptive-measurement strategies for quantum state estimation used to identify a pure state of two-level systems and compare the obtained estimated state to the state obtained when the measurement bases are chosen randomly. The adaptive measurements in this study include the maximum-information-gain strategy, and the confirmation strategy. In this work, we simulate the outcome of the measurement on a system and use the Bayesian estimation method to estimate the obtained state. We then adaptively change the basis of the next measurement in accordance with the estimated state and the used adaptive strategy. We find that the maximum-information-gain strategy provides the fastest convergence and the most accurate state estimation compared to the confirmation strategy and the randomly-chosen-measurement-basis strategy, especially in the presence of measurement noise. Keywords: Quantum state estimation, State tomography, Adaptive measurements