Browsing by Author "Palittapongarnpim, Pantita"

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    Characterization of Magneto-optical Trap For Experiments in Light-Atom Interfacing
    (2012-10-03) Palittapongarnpim, Pantita; Lvovsky, Alexander
    This thesis presents the study of atomic cloud density and temperature in a magneto-optical trap (MOT). The purpose is to find the method in obtaining the densest and coldest cloud the setup can produce. In steady state trap, the highest atomic density possible is that of a cloud in multiple scattering regime where a repulsive force between atoms sets a limit to the density. The number of atoms loaded into the trap is controlled by the trapping beam intensity which also changes the temperature. Therefore the trap density and temperature cannot be controlled separately. The cloud compression is studied as a method of increasing atomic density above what is possible in steady state trap without noticeable influence on the cloud temperature. Cloud compression is also found when the cloud is translated by changing the magnetic field zero-point although in a less predictable fashion than the compression.
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    Evolutionary Algorithm for Adaptive Quantum-Channel Control
    (2019-01-23) Palittapongarnpim, Pantita; Sanders, Barry C.; Wiseman, Howard M.; Simon, Ch; Hobill, David W.; Denzinger, Jörg
    The key to successful implementations of quantum technologies is quantum control, whose aim is to steer quantum dynamics such that the desired outcome is achieved. Quantum control techniques rely on models of the quantum dynamics to generate control policies that attain the control targets. In a practical situation, the dynamic model may not match the dynamic in the implementation, and this mismatch can lead to reduced performance or even a failed control procedure. Data-driven control has been proposed as an alternative to model-based control design. In this approach, measurement outcomes from the system are used to generate a policy, which enables robust control without the need for a noise model. The potential for data-driven quantum control has been demonstrated in the problem of quantum-enhanced adaptive phase estimation. However, the performance and robustness of data-driven policies have never been compared with performance and robustness of model-based control techniques. In this thesis, we aim to determine the advantages and disadvantages of model-based and data-driven policy generation using a simulated quantum-enhanced adaptive phase estimation as an example of a quantum control task. In the process, we explore the connection between an adaptive quantum-enhanced metrological procedure to a decision-making process, which is an alternative model of the dynamic during the control task. We also devise a robust search algorithm based on an evolutionary algorithm that is ignorant of the properties of the phase noise but is still able to deliver quantum-enhanced precision. We then compare the performances of feedback control policies designed using Bayesian inference, which is a model-based technique, to policies generated using this robust evolutionary algorithm on their performance in both noisy and noiseless interferometers. We also assess the resources used in generating and implementing a control policy and use the complexities of the time and space costs as parts of selecting a practical control procedure.