Sampling-based learning control of inhomogeneous quantum ensembles

Chunlin Chen, Daoyi Dong, Ruixing Long, Ian R. Petersen, Herschel A. Rabitz

Research output: Contribution to journalArticlepeer-review

98 Scopus citations


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 Λ-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.

Original languageEnglish (US)
Article number023402
JournalPhysical Review A - Atomic, Molecular, and Optical Physics
Issue number2
StatePublished - Feb 5 2014

All Science Journal Classification (ASJC) codes

  • Atomic and Molecular Physics, and Optics


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