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Adaptive Polarization Pulse Shaping and Modeling of Light-Matter Interactions with Neural Networks
(2007)
The technique of ultrafast polarization shaping is applied to a model quantum system, the potassium dimer. The polarization dependence of the multiphoton ionization dynamics in this molecule is first investigated in pump–probe experiments, and it is then more generally addressed and exploited in an adaptive quantum control experiment utilizing near–IR polarization–shaped laser pulses. The extension of these polarization shaping techniques to the UV spectral range is presented, and methods for the generation and characterization of polarization–shaped laser pulses in the UV are introduced. Systematic scans of double–pulse sequences are introduced for the investigation and interpretation of control mechanisms. This concept is first introduced and illustrated for an optical demonstration experiment, and it is then applied for the analysis of the intrapulse dumping mechanism that is observed in the excitation of a large dye molecule in solution with ultrashort laser pulses. Shaped laser pulses are employed as a means for obtaining copious amounts of data on light–matter interactions. Neural networks are introduced as a novel tool for generating computer–based models for these interactions from the accumulated data. The viability of this approach is first tested for second harmonic generation (SHG) and molecular fluorescence processes. Neural networks are then utilized for modeling the far more complex coherent strong–field dynamics of potassium atoms.
Neural networks can synchronize by learning from each other. For that purpose they receive common inputs and exchange their outputs. Adjusting discrete weights according to a suitable learning rule then leads to full synchronization in a finite number of steps. It is also possible to train additional neural networks by using the inputs and outputs generated during this process as examples. Several algorithms for both tasks are presented and analyzed. In the case of Tree Parity Machines the dynamics of both processes is driven by attractive and repulsive stochastic forces. Thus it can be described well by models based on random walks, which represent either the weights themselves or order parameters of their distribution. However, synchronization is much faster than learning. This effect is caused by different frequencies of attractive and repulsive steps, as only neural networks interacting with each other are able to skip unsuitable inputs. Scaling laws for the number of steps needed for full synchronization and successful learning are derived using analytical models. They indicate that the difference between both processes can be controlled by changing the synaptic depth. In the case of bidirectional interaction the synchronization time increases proportional to the square of this parameter, but it grows exponentially, if information is transmitted in one direction only. Because of this effect neural synchronization can be used to construct a cryptographic key-exchange protocol. Here the partners benefit from mutual interaction, so that a passive attacker is usually unable to learn the generated key in time. The success probabilities of different attack methods are determined by numerical simulations and scaling laws are derived from the data. If the synaptic depth is increased, the complexity of a successful attack grows exponentially, but there is only a polynomial increase of the effort needed to generate a key. Therefore the partners can reach any desired level of security by choosing suitable parameters. In addition, the entropy of the weight distribution is used to determine the effective number of keys, which are generated in different runs of the key-exchange protocol using the same sequence of input vectors. If the common random inputs are replaced with queries, synchronization is possible, too. However, the partners have more control over the difficulty of the key exchange and the attacks. Therefore they can improve the security without increasing the average synchronization time.