Date on Master's Thesis/Doctoral Dissertation
Computer Engineering and Computer Science
Computer Science, MS
Sierrasosa, Daniel Esteban
Committee Co-Chair (if applicable)
Imam, Ibrahim Najati
Yampolskiy, Roman V.
Jayanthi, Chakram S.
Quantum Computing; Quantum Information; Quantum Machine Learning; Amplitude Encoding; State Preparation
Quantum computing performs calculations by using physical phenomena and quantum mechanics principles to solve problems. This form of computation theoretically has been shown to provide speed ups to some problems of modern-day processing. With much anticipation the utilization of quantum phenomena in the field of Machine Learning has become apparent. The work here develops models from two software frameworks: TensorFlow Quantum (TFQ) and PennyLane for machine learning purposes. Both developed models utilize an information encoding technique amplitude encoding for preparation of states in a quantum learning model. This thesis explores both the capacity for amplitude encoding to provide enriched state preparation in learning methods and a deep analysis of data properties that provide insights into training data using a Variational Quantum Classifier (VQC). The advent of these new methods begs the question of how to best use these tools, we aim to give some overview explanation for the applicable state of quantum machine learning given actual device constraints. The results show there is a clear advantage for using amplitude encoding over other methods as we show using a hybrid quantum-classical neural network in TFQ. Additionally, there are several steps of preprocessing that can lead to more feature rich data when utilizing a VQC, in essence the no free lunch theorem holds true for quantum learning methods as it does in classical techniques. Information albeit encoded in a quantum form does not change the steps of preparing data but involves new ways to comprehend and appreciate these novel methods.
Telahun, Michael, "Exploring Information for Quantum Machine Learning Models" (2020). Electronic Theses and Dissertations. Paper 3433.