Scalable techniques for quantum network engineering
Abstract/Contents
- Abstract
- In the quest for creating ``quantum enhanced'' systems for information processing many currently pursued design strategies are difficult to scale significantly beyond a few dozen qubits. The dominant design paradigm relies on starting with near perfect quantum components and a vast overhead of classical external control. In my thesis I present tools and methods for a more integrated framework which treats quantum and hybrid quantum-classical systems on equal footing. We have recently defined a Quantum Hardware Description Language (QHDL) capable of describing networks of interconnected open quantum systems. QHDL is compiled to symbolic and numerical system models by a custom software tool suite named QNET. This allows us to rapidly iterate over quantum network designs and derive the associated equations of motion. Building on a recently developed model reduction technique for describing networks of nonlinear oscillators in the semi-classical regime, I present a library of nonlinear optical circuit designs useful for all-optical computation. I further present an end-to-end theoretical proposal to create all-optical neuromorphic circuits capable of supervised learning. The system is hierarchically composed of tunable linear amplifiers, analog phase memories and thresholding non-linear circuits which can be used to construct more general quantum feedback networks for nonlinear information processing. Finally, I introduce a novel model transformation capable of dividing the description of quantum states into a low-dimensional quasi-classical part coupled to a lower complexity quantum state. This approach is exact and naturally tailored to simulating coupled quantum systems with varying degrees of dissipation.
Description
| Type of resource | text |
|---|---|
| Form | electronic; electronic resource; remote |
| Extent | 1 online resource. |
| Publication date | 2016 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Associated with | Tezak, Nikolas |
|---|---|
| Associated with | Stanford University, Department of Applied Physics. |
| Primary advisor | Mabuchi, Hideo |
| Thesis advisor | Mabuchi, Hideo |
| Thesis advisor | Ganguli, Surya, 1977- |
| Thesis advisor | Hayden, Patrick (Patrick M.) |
| Thesis advisor | Vuckovic, Jelena |
| Advisor | Ganguli, Surya, 1977- |
| Advisor | Hayden, Patrick (Patrick M.) |
| Advisor | Vuckovic, Jelena |
Subjects
| Genre | Theses |
|---|
Bibliographic information
| Statement of responsibility | Nikolas Tezak. |
|---|---|
| Note | Submitted to the Department of Applied Physics. |
| Thesis | Thesis (Ph.D.)--Stanford University, 2016. |
| Location | electronic resource |
Access conditions
- Copyright
- © 2016 by Nikolas Anton Tezak
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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