Date Thesis Awarded
5-2022
Access Type
Honors Thesis -- Open Access
Degree Name
Bachelors of Science (BS)
Department
Computer Science
Advisor
Qun Li
Committee Members
Bin Ren
Stephen Trefethen
Abstract
This thesis explores basic concepts of machine learning, neural networks, federated learning, and quantum computing in an effort to better understand Quantum Machine Learning, an emerging field of research. We propose Quantum Federated Learning (QFL), a schema for collaborative distributed learning that maintains privacy and low communication costs. We demonstrate the QFL framework and local and global update algorithms with implementations that utilize TensorFlow Quantum libraries. Our experiments test the effectiveness of frameworks of different sizes. We also test the effect of changing the number of training cycles and changing distribution of training data. This thesis serves as a synoptic summary of the author's senior honors research starting in the summer of 2021 and culminating in the spring of 2022.
Recommended Citation
Brei, Anneliese, "Quantum Federated Learning: Training Hybrid Neural Networks Collaboratively" (2022). Undergraduate Honors Theses. William & Mary. Paper 1756.
https://scholarworks.wm.edu/honorstheses/1756
Included in
Artificial Intelligence and Robotics Commons, Other Computer Sciences Commons, Quantum Physics Commons, Theory and Algorithms Commons
