Exploring Associative Memory Performance of Confocal Cavity QED Network
Abstract/Contents
- Abstract
- Driven-dissipative systems have emerged as featured models of study between the domains of physics, mathematics, and computer science, notably atomic molecular and optical (AMO) physics and quantum information. The ability of these systems to interact with their external environment heralds applications in quantum computing, quantum communication, metrology, and sensing via phenomena such as natural error correction. We explore a candidate experimental framework for achieving driven-dissipative computation in the form of an associative memory, a neural network capable of pattern storage and recollection. This platform is realized by a confocal cavity QED apparatus, where the modes of this optical cavity behave as synapses, mediating interactions between a network of spinful bosonic ensembles which function as neurons. In this thesis we investigate the capabilities of this multimode cavity system to perform memory retrieval where the intracavity spin relaxation dynamics are modeled according to a mean-field treatment. We confirm (from previous works by Marsh et. all) that the system minimizes energy according to deterministic steepest descent dynamics instead of Glauber dynamics, in concert with previous results which do not utilize a mean field approach. Furthermore, these dynamics were predicted to occur at faster timescales than the previous non mean-field treatment. We also explore the efficiency of memory recollection over expanded ranges of input errors when the system is subjected to auxiliary longitudinal fields. We observed that these longitudinal fields performed state preparation effectively, and provided a slight improvement in memory recall contingent upon the appropriate tuning.
Description
| Type of resource | text |
|---|---|
| Date modified | December 5, 2022 |
| Publication date | June 9, 2022; June 8, 2022 |
Creators/Contributors
| Author | Getachew, Yosheb |
|---|---|
| Degree granting institution | Stanford University |
| Department | Department of Physics |
| Thesis advisor | Lev, Benjamin |
| Thesis advisor | Safavi-Naeini, Amir |
Subjects
| Subject | associative memory |
|---|---|
| Subject | cavity |
| Subject | mean-field |
| Genre | Text |
| Genre | Thesis |
Bibliographic information
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- License
- This work is licensed under a Creative Commons Attribution Non Commercial 4.0 International license (CC BY-NC).
Preferred citation
- Preferred citation
- Getachew, Y. (2022). Exploring Associative Memory Performance of Confocal Cavity QED Network. Stanford Digital Repository. Available at https://purl.stanford.edu/qn178kb2231
Collection
Undergraduate Theses, Department of Physics
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- Contact
- yoshebg@stanford.edu
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