Brett J. Pearson

Coherent control using adaptive learning algorithms

We have constructed an automated learning apparatus to control quantum systems. By directing intense shaped ultrafast laser pulses into a variety of samples and using a measurement of the system as a feedback signal, we are able to reshape the laser pulses to direct the system into a desired state. The feedback signal is the input to an adaptive learning algorithm. This algorithm programs a computer-controlled, acousto-optic modulator pulse shaper. The learning algorithm generates new shaped laser pulses based on the success of previous pulses in achieving a predetermined goal.

Coherent learning control of vibrational motion in room temperature molecular gases

An evolutionary learning algorithm in conjunction with an ultrafast optical pulse shaper was used to control vibrational motion in molecular gases at room temperature and high pressures. We demonstrate mode suppression and enhancement in sulfur hexafluoride and mode selective excitation in carbon dioxide. Analysis of optimized pulses discovered by the algorithm has allowed for an understanding of the control mechanism.

Extracting quantum dynamics from genetic learning algorithms through principal control analysis

Genetic learning algorithms are widely used to control ultrafast optical pulse shapes for photo-induced quantum control of atoms and molecules. An unresolved issue is how to use the solutions found by these algorithms to learn about the systems quantum dynamics. We propose a simple method based on covariance analysis of the control space, which can reveal the degrees of freedom in the effective control Hamiltonian. We have applied this technique to stimulated Raman scattering in liquid methanol. A simple model of two-mode stimulated Raman scattering is consistent with the results.

Spectral phase effects on nonlinear resonant photochemistry of 1,3-cyclohexadiene in solution

We have investigated the ring opening of 1,3-cyclohexadiene to form 1,3,5-cis-hexatriene (Z-HT) using optical pulse shaping to enhance multiphoton excitation. A closed-loop learning algorithm was used to search for an optimal spectral phase function, with the effectiveness or fitness of each optical pulse assessed using the UV absorption spectrum. The learning algorithm was able to identify pulses that increased the formation of Z-HT by as much as a factor of 2 and to identify pulse shapes that decreased solvent fragmentation while leaving the formation of Z-HT essentially unaffected. The highest yields of Z-HT did not occur for the highest peak intensity laser pulses. Rather, negative quadratic phase was identified as an important control parameter in the formation of Z-HT.

Control of Raman Lasing in the Nonimpulsive Regime

We explore coherent control of stimulated Raman scattering in the nonimpulsive regime. Optical pulse shaping of the coherent pump field leads to control over the stimulated Raman output. A model of the control mechanism is investigated.

Note: self-characterizing ultrafast pulse shaper for rapid pulse switching.

We use an acousto-optic modulator at the input to a two-dimensional, Fourier-domain pulse shaper to achieve built-in characterization of the output pulses with spectral interferometry. The device is capable of rapid switching between pulse shapes.

Principal Control Analysis: Gaining Insight from Feedback Learning Algorithms

Feedback learning algorithms are widely used to search for optical pulse shapes for quantum control. We propose a simple analysis of the pulse shapes to learn about the degrees of freedom in the effective control Hamiltonian. When applied to stimulated Raman scattering in methanol, the technique yields results consistent with a simple model of two-mode SRS in methanol.