Quantum-state estimation by self-learning measurements

We describe strategies for quantum-state estimation based on self-learning algorithms. In contrast to the optimal estimation procedures proposed earlier, our schemes rely on measurements performed separately on each quantum system in a finite ensemble. We numerically simulate our strategies in the case of finite ensembles of qubits and compare the resulting average fidelities to the fidelity of optimal quantum-state estimation.