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Sampling-based learning control of inhomogeneous quantum ensembles

Author(s): Chen, Chunlin; Dong, Daoyi; Long, Ruixing; Petersen, Ian R.; Rabitz, Herschel A.

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Abstract: Compensation for parameter dispersion is a significant challenge for control of inhomogeneous quantum ensembles. In this paper, we present the systematic methodology of sampling-based learning control (SLC) for simultaneously steering the members of inhomogeneous quantum ensembles to the same desired state. The SLC method is employed for optimal control of the state-to-state transition probability for inhomogeneous quantum ensembles of spins as well as Lambda-type atomic systems. The procedure involves the steps of (i) training and (ii) testing. In the training step, a generalized system is constructed by sampling members according to the distribution of inhomogeneous parameters drawn from the ensemble. A gradient flow based learning and optimization algorithm is adopted to find an optimal control for the generalized system. In the process of testing, a number of additional ensemble members are randomly selected to evaluate the control performance. Numerical results are presented, showing the effectiveness of the SLC method.
Publication Date: 5-Feb-2014
Citation: Chen, Chunlin, Dong, Daoyi, Long, Ruixing, Petersen, Ian R., Rabitz, Herschel A. (2014). Sampling-based learning control of inhomogeneous quantum ensembles. PHYSICAL REVIEW A, 89 (10.1103/PhysRevA.89.023402
DOI: doi:10.1103/PhysRevA.89.023402
ISSN: 2469-9926
EISSN: 2469-9934
Pages: 023402-1 - 023402-7
Type of Material: Journal Article
Journal/Proceeding Title: PHYSICAL REVIEW A
Version: Final published version. Article is made available in OAR by the publisher's permission or policy.

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