An optimized laser pulse that allows to control the lenght of a chemical bond (or to create another bonds).
copyright Gabriel Turinici 2010
Algorithms for experimental realization of quantum control
Experimental realization of quantum control is done in practice through the online minimization of a cost functional that is computed by measurements; the minimization algorithm used is most often a derivative of Genetic Algorithms or Evolutionary Strategy paradigm. The understanding of how this algorithm manage to find the good solutions and its practical implications at the goal of this part of my research
Controllability in bilinear quantum chemistry models
The control of quantum phenomena has been demonstrated on a variety of experimental setings. However, fundamental questions remains still unanswered in this field such as the good notions of controllability of the infinite-dimensional equations and also the search for the easy implementable criterions to assess this controllability property. Some results may be adapted from the already obtained experience with the finite-dimensional setings.
Monotonic algorithms for quantum optimal control
Laser control of complex molecular and solid-state systems is becoming feasible, especially since the introduction of closed loop laboratory learning techniques and their successful implementation. However, at the level of the numerical simulations, much work is still to be done in order to bring the size of the systems that can be treated accurately to practical dimensions. Of course, using a good algorithm to solve the quantum control critical point equations is very important to the reduction of the overall cost; some of the most used algorithms nowadays falls within the class of "monotonically convergent" algorithms that are guaranteed to improve the performance (measured through a cost functional) a each step. My work in this area aims at describing new classes of monotonic algorithms and also at further studying their convergence properties.
Dynamical discrimination of molecules
Similar molecules often may be characterized as sharing common chemical structures made up of the same atomic components. Such molecules are expected to have related Hamiltonians, and thus similar chemical and physical properties. Examples range from simple isotopic variants of diatomics (e.g., 79Br2, 81Br2) and isomers (e.g., cis- and trans-1,2-dichloroethylene) to highly complex molecules including those of biological relevance. A common need is to analyze or separate one molecular species in the presence of possibly many other similar agents. This problem often demands rapid, sensitive, and dependable identification or purification measures. Traditional approaches mainly focus on exploiting the subtle differences in the microscopic properties, or macroscopic properties; these methods have seen wide applications, but they may all be characterized as static or one-dimensional with their capabilities being pushed to the limit. To enhance the ability to distinguish molecules, a new paradigm is proposed aiming for optimal discrimination by actively amplifying the seemingly subtle differences between similar molecules. The proposed optimal dynamic discrimination (ODD) approach exploits the richness of quantum molecular dynamics. Although the dynamics of similar quantum systems are governed by related Hamiltonians, each species could evolve in a distinct fashion under the same properly tailored external control, e.g. a laser field. The wave packets of the similar molecules in the system are excited by a common laser pulse, which is tailored with the goal of inducing signals (possibly detected with another common laser pulse) from only one species, while suppressing signals from all the others. Optimal control techniques are potentially ideal tools for implementing ODD, as the underlying closed loop learning control process inherently operates based on achieving discrimination between one dynamical process versus another.
