A self-guided algorithm for learning control of quantum-mechanical systems

This paper presents a general self-guided algorithm for direct laboratory l earning of controls to manipulate quantum-mechanical systems. The primary f ocus is on an algorithm based on the learning of a linear laboratory input- output map from a sequence of controls, and their observed impact on the qu antum-mechanical system. This map is then employed in an iterative fashion, to sequentially home in on the desired objective. The objective may be a t arget state at a final time, or a continuously weighted observational traje ctory. The self-guided aspects of the algorithm are based on implementing a cost functional that only contains laboratory-accessible information. Thro ugh choice of the weights in this functional, the algorithm can automatical ly stay within the bounds of each local linear map and indicate when a new map is necessary for additional iterative improvement. Finally, these conce pts can be generalized to include the possibility of employing nonlinear ma ps, as well as just the laboratory control instrument settings, rather than observation of the control itself. An illustrative simulation of the conce pts is presented for the control of a four-level quantum system. (C) 1999 A merican Institute of Physics. [S0021-9606(99)00301-3].