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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.|
|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|
|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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