Experimental adaptive process tomography

Abstract

Adaptive measurements were recently shown to significantly improve the performance of quantum state tomography. Utilizing information about the system for the online choice of optimal measurements allows one to reach the ultimate bounds of precision for state reconstruction. In this article we generalize an adaptive Bayesian approach to the case of process tomography and experimentally show its superiority in the task of learning unknown quantum operations. Our experiments with photonic polarization qubits cover all types of single-qubit channels. We also discuss instrumental errors and the criteria for evaluation of the ultimate achievable precision in an experiment. It turns out that adaptive tomography provides a lower noise floor in the presence of strong technical noise.