Journal
PHYSICAL REVIEW A
Volume 89, Issue 2, Pages -Publisher
AMER PHYSICAL SOC
DOI: 10.1103/PhysRevA.89.023402
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Funding
- Natural Science Foundation of China [61273327, 61374092]
- Australian Research Council [DP130101658, FL110100020]
- U.S. Department of Energy [DE-FG02-02ER15344]
- NSF [CHE-0718610]
- Army Research Office [0025020]
- Division Of Chemistry
- Direct For Mathematical & Physical Scien [1058644] Funding Source: National Science Foundation
- U.S. Department of Energy (DOE) [DE-FG02-02ER15344] Funding Source: U.S. Department of Energy (DOE)
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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.
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