2026-09-19T18:20:24Z https://www.ideals.illinois.edu/oai-pmh

oai:www.ideals.illinois.edu:2142/132450 2026-02-20 col_2142_5131 col_2142_8888 com_2142_5130 com_2142_8887 com_2142_234

application/pdf en text Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2026-02-19 without embargo terms The student, Yueze Liu, accepted the attached license on 2025-07-22 at 04:18. The student, Yueze Liu, submitted this Thesis for approval on 2025-07-22 at 04:21. This Thesis was approved for publication on 2025-10-02 at 09:35. DSpace SAF Submission Ingestion Package generated from Vireo submission #22688 on 2026-02-19 at 18:24:03 Investigation of machine learning techniques in stabilization of co-propagating polarization encoded photons Liu, Yueze 2025-10-02 Chitambar, Eric Quantum communication Polarization encoding Machine learning Quantum signal stabilization Reinforcement learning Time-series prediction Quantum-classical integration Channel calibration Quantum networks Environmental noise compensation eng Quantum communication protocols and networks have advanced considerably in experimental laboratories; however, their operation is frequently constrained by short time frames and significant environmental noise. These challenges affect both time-coded and polarization-encoded photon systems. Polarization encoding has become the prevalent method despite its intrinsic issues with stability. This thesis investigates the integration of classical and quantum signals through the application of modern machine learning techniques, with the goal of overcoming the stochastic challenges that have historically impeded system performance. We evaluate three distinct schemes: (1) direct prediction of the quantum signal from the classical signal, (2) variable recalibration queue for the quantum channel, and (3) calibration of the quantum signal via reinforcement learning. Each approach is characterized by unique strengths and limitations in terms of measurement overhead, implementation complexity, and operational stability. Through extensive experimental testing over durations ranging from hours to days, we analyze the performance of attention-based models, sliding window time series predictors, and reinforcement learning frameworks in predicting and stabilizing polarization states. Our findings indicate the potential of machine learning approaches to enhance the longevity and reliability of quantum communication systems, while also highlighting the challenges of model generalization and data requirements for robust performance. This research contributes to the advancement of quantum communication infrastructure by demonstrating practical methods for maintaining stable polarization encoded quantum channels, essential for the development of long-distance secure quantum networks. 2025-12 Thesis https://hdl.handle.net/2142/132450 Copyright 2025 Yueze Liu Electrical & Computer Eng Electrical & Computer Engr University of Illinois Urbana-Champaign M.S. Thesis