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Bulletin of the American Physical Society

Quantum process tomography of energy and phase relaxation through adaptive measurements

Markku P. V. Stenberg, Frank K. Wilhelm

Published Mar 18, 2013• 0 citations
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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.
Adaptive measurements have recently been shown to significantly improve the performance of quantum state and process tomography. Adaptive tomography is especially useful in complicated experiments with low outcome generation rate, since it allows one to extract maximal information from a limited amount of data. However, the existing methods either cannot be simply applied to high-dimensional systems or are prohibitively computationally expensive. Here we propose and experimentally implement a tomographic protocol specially designed for the reconstruction of high-dimensional quantum states. The protocol shows qualitative improvement in infidelity scaling with the number of measurements and is fast enough to allow for complete state tomography of states with dimensionality up to 36.