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Journal of Mathematical Chemistry

Systematically altering the apparent topology of constrained quantum control landscapes

Ashley Donovan, HERSCHEL A. RABITZ

Published Dec 23, 2014• 4 citations
Publisher
DOI
10.1007/s10910-014-0453-7

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A search for the variables that control a quantum system's dynamics occurs over a landscape, defined as the target objective as a function of the variables. Prior studies show that upon satisfaction of three specific assumptions, the topology of the landscape is free of suboptimal traps that could prematurely halt the search for an optimal control. One key assumption is free access to all necessary control variables; however, in practice, the controls are always limited in some fashion which may result in constraint-induced traps on the landscape. This paper aims to introduce the means to systematically explore the nature of constrained controls that yield suboptimal outcomes. The procedure utilizes kinematic controls, which comprise a simple set of time-independent variables, and then performs a landscape topology-preserving transformation into corresponding dynamic controls. The equivalent landscape topology of these two formulations permits the study of a family of dynamic controls that reflect constrained control landscape behavior. In particular, constrained dynamic controls are identified as isolated points on the landscape or as suboptimal level sets. The wide range of such dynamic controls indicates the richness and complexity of constraint-induced features on the landscape.
The growing success of controlling the dynamics of quantum systems has been ascribed to the favorable topology of the quantum control landscape, which represents the physical observable as a function of the control field. The landscape contains no suboptimal trapping extrema when reasonable physical assumptions are satisfied, including that no significant constraints are placed on the control resources. A topic of prime interest is understanding the effects of control field constraints on the apparent landscape topology, as constraints on control resources are inevitable in the laboratory. This work particularly explores the effects of constraining the control field fluence on the topology and features of the control landscape for pure-state population transfer in a two-level system through numerical simulations, where unit probability population transfer in the system is only accessible in the strong coupling regime within the model explored here. With the fluence and three phase variables used for optimization, no local optima are found on the landscape, although saddle features are widespread at low fluence values. Global landscape optima are found to exist at two disconnected regions of the fluence that possess distinct topologies and structures. Broad scale connected optimal level sets are found when the fluence is sufficiently large, while the connectivity is reduced as the fluence becomes more constrained. These results suggest that seeking optimal fields with constrained fluence or other resources may encounter complex landscape features, calling for sophisticated algorithms that can efficiently find optimal controls.