Subject Area
Computer Engineering
Abstract
Quantum computing enables new approaches to data processing, especially in quantum machine learning. Unlike classical systems, quantum data must be synthesized through operations and can exist in superposition. Encoding choices affect efficiency, noise resilience, and trainability—key factors in quantum machine learning models. This dissertation enhances quantum data encodings by extending quantum read-only memory (QROM) beyond binary representations, improving efficiency and parallelism. It introduces new compilation methods for quantum random number generators (QRNGs), supporting non-parametric distributions for post-quantum cryptography. Additionally, it explores Cayley graph-based encodings to extract spectral features for quantum machine learning.
Degree Date
Spring 5-17-2025
Document Type
Dissertation
Degree Name
Ph.D.
Department
Computer Science and Engineering
Advisor
Mitchell A. Thornton
Number of Pages
28
Format
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial 4.0 License
Recommended Citation
Sinha, Aviraj, "Data Encoding, Compilation, and Algorithms for Quantum Machine Learning" (2025). Computer Science and Engineering Theses and Dissertations. 48.
https://scholar.smu.edu/engineering_compsci_etds/48
Included in
Artificial Intelligence and Robotics Commons, Data Science Commons, Quantum Physics Commons, Theory and Algorithms Commons
