Photonic computing architectures for classical and quantum information processing
Abstract/Contents
- Abstract
- As progress in traditional electronic computing systems approaches fundamental physical limits, we must explore alternative approaches for further growth in computing power. Photonics is a promising hardware platform for many emerging computing technologies, including optical neural networks and quantum computation. In this thesis, I will present several novel designs for light-based computing systems. First, I will discuss several advancements we have made in nanophotonic neural networks, including design and experimental realization of electro-optic nonlinear activation functions, and architectures and initialization routines for programmable linear optical devices. Next, I will present two novel schemes for quantum information processing: a programmable photonic gate array which can be dynamically reconfigured to prepare any quantum state, and an architecture for an optical quantum computer which can perform any calculation using only a single directly controllable qubit. Finally, I will discuss a design for a photonic quantum emulator capable of simulating the dynamics of a broad class of Hamiltonians in lattices with arbitrary dimensions and topologies.
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 | Bartlett, Benjamin C |
|---|---|
| Degree supervisor | Fan, Shanhui, 1972- |
| Thesis advisor | Fan, Shanhui, 1972- |
| Thesis advisor | Safavi-Naeini, Amir H |
| Thesis advisor | Solgaard, Olav |
| Degree committee member | Safavi-Naeini, Amir H |
| Degree committee member | Solgaard, Olav |
| Associated with | Stanford University, Department of Applied Physics |
Subjects
| Genre | Theses |
|---|---|
| Genre | Text |
Bibliographic information
| Statement of responsibility | Ben Bartlett. |
|---|---|
| Note | Submitted to the Department of Applied Physics. |
| Thesis | Thesis Ph.D. Stanford University 2022. |
| Location | https://purl.stanford.edu/ht812kd2819 |
Access conditions
- Copyright
- © 2022 by Benjamin C Bartlett
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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