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

Liu, Yueze

Permalink

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

Description

Title

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

Author(s)

Liu, Yueze

Issue Date

2025-10-02

Director of Research (if dissertation) or Advisor (if thesis)

Chitambar, Eric

Department of Study

Electrical & Computer Eng

Discipline

Electrical & Computer Engr

Degree Granting Institution

University of Illinois Urbana-Champaign

Degree Name

M.S.

Degree Level

Thesis

Keyword(s)

• Quantum communication
• Polarization encoding
• Machine learning
• Quantum signal stabilization
• Reinforcement learning
• Time-series prediction
• Quantum-classical integration
• Channel calibration
• Quantum networks
• Environmental noise compensation

Abstract

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.

Graduation Semester

2025-12

Type of Resource

Thesis

Handle URL

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

Copyright and License Information

Copyright 2025 Yueze Liu

Owning Collections