Machine Learning and Optimization Techniques for Trapped-ion Quantum Simulators
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In recent years, quantum simulators have been the focus of intense research due to their potential in unraveling the inner workings of complex quantum systems. The exponential scaling of the Hilbert space of quantum systems limits the capabilities of classical approaches. Quantum simulators, on the other hand, are suited to efficiently emulate these quantum systems for in-depth research. Out of the various platforms for quantum simulators, trapped-ions have been prized for their long coherence times, exceptional initialization and detection fidelities and their innate full-connectivity. In the development of the trapped-ion apparatus as a quantum simulator, numerous optimization problems emerge. In this thesis, we examine, develop and refine the strategies available to us for addressing such problems. A class of techniques of particular interest to us is the set of machine learning algorithms. This can be attributed to their capabilities at identifying correlations in massive data sets. Specifically, we describe how artificial neural networks can be employed to assist in the programming of the trapped-ion system as an arbitrary spin model quantum simulator. In addition, we present an augmentation to the trapped-ion architecture, utilizing optical tweezers, that enables the programmable manipulation of phonon modes. We describe the protocols developed to effectively program the phonon modes and propose an application of the scheme for enhancing the performance of multi-species systems. Finally, we explore the use of machine learning algorithms to perform state readout of the trapped-ion system. The work in this thesis extends the utility of the trapped-ion system for performing quantum information processing experiments.
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Yi Hong Teoh (2021). Machine Learning and Optimization Techniques for Trapped-ion Quantum Simulators. UWSpace. http://hdl.handle.net/10012/17322