Date of Thesis
Spring 2022
Description
Tensor isomorphism is a hard problem in computational complexity theory. Tensor isomorphism arises not just in mathematics, but also in other applied fields like Machine Learning, Cryptography, and Quantum Information Theory (QIT). In this thesis, we develop a new approach to testing (non)-isomorphism of tensors that uses local information from "contractions" of a tensor to detect differences in global structures. Specifically, we use projective geometry and tensor contractions to create a labelling data structure for a given tensor, which can be used to compare and distinguish tensors. This contraction labelling isomorphism test is quite general, and its practical potential remains largely unexplored. As a proof of concept, however, we apply the technique to a very recent classification of 4-qubit states in QIT.
Keywords
Tensor isomorphism, tensor contraction, projective geometry
Access Type
Honors Thesis
Degree Type
Bachelor of Arts
Major
Mathematics
First Advisor
Peter Brooksbank
Recommended Citation
Kieu, Anh, "A Contraction Based Approach to Tensor Isomorphism" (2022). Honors Theses. 621.
https://digitalcommons.bucknell.edu/honors_theses/621