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 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 language | English (US) |
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Article number | 023402 |
Journal | Physical Review A - Atomic, Molecular, and Optical Physics |
Volume | 89 |
Issue number | 2 |
DOIs | |
State | Published - Feb 5 2014 |
All Science Journal Classification (ASJC) codes
- Atomic and Molecular Physics, and Optics