Sampling-based learning control of inhomogeneous quantum ensembles
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.
- Publication:
-
Physical Review A
- Pub Date:
- February 2014
- DOI:
- 10.1103/PhysRevA.89.023402
- arXiv:
- arXiv:1308.1454
- Bibcode:
- 2014PhRvA..89b3402C
- Keywords:
-
- 03.67.-a;
- 02.30.Yy;
- 03.67.Pp;
- Quantum information;
- Control theory;
- Quantum error correction and other methods for protection against decoherence;
- Quantum Physics
- E-Print:
- 8 pages, 9 figures