Leveraging near-term quantum computing for spin chemistry and combinatorial optimization

Placeholder Show Content

Abstract/Contents

Abstract
Quantum computing offers computational tools and speed-ups beyond the reach of classical computers in the long run, yet demonstrating the computational advantage of small-scale near-term quantum devices remains a challenge today. In this thesis, I introduce two applications of near-term quantum computers from the fields of spin chemistry and combinatorial optimization. In the first part of my thesis, I discuss the quantum beats phenomena that occur in spin chemistry when geminate radical pairs go through singlet-to-triplet conversions under hyperfine coupling interactions and an external magnetic field. I demonstrate that both the coherent time evolution and the thermal relaxation of these radical pair systems can be efficiently simulated on a quantum computer, both hardware and simulator. I introduce the first Hamiltonian simulation method on a quantum computer that can simulate the quantum beats phenomenon for radical pair systems with nontrivial hyperfine interactions under an arbitrary magnetic field, by utilizing Hamiltonian partitioning strategies. The second part of my thesis focuses on the open-pit mining problem, where the optimal mine digging pattern that maximizes the profit is searched for, while obeying slope constraints. This optimization is traditionally solved as a maximum closure problem. Our algorithm formulates this task as a Hamiltonian ground state search in order to map it to a quantum computer, and utilizes VQE as a subroutine to find the optimal solution. I show that the Hamiltonian can be decomposed into smaller, classically correlated partitions that can each be iteratively optimized. This will allow us to make use of near-term quantum hardware for larger scale optimization problems.

Description

Type of resource text
Form electronic resource; remote; computer; online resource
Extent 1 online resource.
Place California
Place [Stanford, California]
Publisher [Stanford University]
Copyright date 2022; ©2022
Publication date 2022; 2022
Issuance monographic
Language English

Creators/Contributors

Author Tolunay, Emine Meltem
Degree supervisor Weissman, Tsachy
Thesis advisor Weissman, Tsachy
Thesis advisor Bouland, Adam
Thesis advisor Jones, Barbara
Degree committee member Bouland, Adam
Degree committee member Jones, Barbara
Associated with Stanford University, Department of Electrical Engineering

Subjects

Genre Theses
Genre Text

Bibliographic information

Statement of responsibility Emine Meltem Tolunay.
Note Submitted to the Department of Electrical Engineering.
Thesis Thesis Ph.D. Stanford University 2022.
Location https://purl.stanford.edu/sv638nh7935

Access conditions

Copyright
© 2022 by Emine Meltem Tolunay

Also listed in

Loading usage metrics...