Person:

Perdomo, Alejandro

We provide an intuitive platform for engineering exciton transfer dynamics. We show that careful consideration of the spectral density, which describes the system-bath interaction, leads to opportunities to engineer exciton transfer. Since excitons in nanostructures are proposed for use in quantum information processing and artificial photosynthetic designs, our approach paves the way for engineering a wide range of desired exciton dynamics. We carefully describe the validity of the model and use experimentally relevant material parameters to show counter-intuitive examples of directed exciton transfer in a linear chain of quantum dots.

In this paper we explore the use of a quantum optimization algorithm for obtaining low-energy conformations of protein models. We discuss mappings between protein models and optimization variables, which are in turn mapped to a system of coupled quantum bits. General strategies are given for constructing Hamiltonians to be used to solve optimization problems of physical, chemical, or biological interest via quantum computation by adiabatic evolution. As an example, we implement the Hamiltonian corresponding to the hydrophobic-polar model for protein folding. Furthermore, we present an approach to reduce the resulting Hamiltonian to two-body terms gearing toward an experimental realization.