Mainz 2017 – wissenschaftliches Programm
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Q: Fachverband Quantenoptik und Photonik
Q 23: Quantum Information: Concepts and Methods IV
Q 23.3: Vortrag
Dienstag, 7. März 2017, 15:00–15:15, P 2
Superfast maximum likelihood reconstruction for quantum tomography — •Jiangwei Shang1,2, Zhengyun Zhang3, and Hui Khoon Ng1,4,5 — 1Centre for Quantum Technologies, National University of Singapore, Singapore 117543, Singapore — 2Naturwissenschaftlich-Technische Fakultät, Universität Siegen, Walter-Flex-Straße 3, 57068 Siegen, Germany — 3BioSyM IRG, Singapore-MIT Alliance for Research and Technology (SMART) Centre, Singapore 138602, Singapore — 4Yale-NUS College, Singapore 138527, Singapore — 5MajuLab, CNRS-UNS-NUS-NTU International Joint Research Unit, UMI 3654, Singapore
Conventional methods for computing maximum-likelihood estimators (MLE) often converge slowly in practical situations, leading to a search for simplifying methods that rely on additional assumptions for their validity. In this work, we provide a fast and reliable algorithm for MLE reconstruction that avoids this slow convergence. Our method utilizes an accelerated projected-gradient scheme that allows one to accommodate the quantum nature of the problem in a different way. We demonstrate the power of our approach by comparing its performance with other algorithms for n-qubit state tomography. In particular, an 8-qubit situation that purportedly took weeks of computation time in 2005 can now be completed in under a minute for a single set of data, with far higher accuracy than previously possible. The same algorithm can be applied to general optimization problems over the quantum state space; the philosophy of projected gradients can further be utilized for optimization contexts with general constraints.