Investigation of machine learning techniques in stabilization of co-propagating polarization encoded photons

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.

Name thesis:degree_name

M.S.

Level thesis:degree_level

Thesis

Discipline thesis:degree_discipline

Electrical & Computer Engr

Grantor

University of Illinois Urbana-Champaign

Year dc:date

2025

Author dc:creator

• Liu, Yueze

Contributors dc:contributor

• 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

Statement dc:rights

• Copyright 2025 Yueze Liu

Language dc:language

en

Handle dc:identifier

https://hdl.handle.net/2142/132450

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/132450