Sanders, Barry C.Palittapongarnpim, Pantita2019-01-242019-01-242019-01-23Palittapongarnpim, P. (2019). Evolutionary Algorithm for Adaptive Quantum-Channel Control (Doctoral thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca.http://hdl.handle.net/1880/109509The 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.enUniversity of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission. In reference to IEEE copyrighted material which is used with permission in this thesis, the IEEE does not endorse any of the University of Calgary’s products or services. Internal or personal use of this material is permitted. If interested in reprinting/republishing IEEE copyrighted material for advertising or promotional purposes or for creating new collective works for resale or redistribution, please go to http://www.ieee.org/publications_standards/publications/rights/rights_link.html to learn how to obtain a License from RightsLink. If applicable, University Microfilms and/or ProQuest Library, or the Archives of Canada may supply single copies of the dissertation.Quantum metrologyEvolutionary algorithmMachine learningQuantum controlQuantum-enhanced adaptive phase estimationData-driven controlPhysicsPhysics--TheoryApplied SciencesComputer Sciencedoctoral thesis10.11575/PRISM/35768