Coherent Control of Retinal Isomerization in Bacteriorhodopsin in the High Intensity Regime

Abstract

We use a learning algorithm to optimize retinal isomerization in bacteriorhodopsin. At low energies the learning algorithm fails to converge. At higher energies it converges to a transform-limited pulse with the photoproduct yield increasing linearly with excitation energy beyond the saturation of the first excited state. The results are interpreted including the influence of one-photon and multiphoton transitions.