Quantum machine learning: a theoretical study-case and practical implementation of PCA using quantum computing
Quantum computing is an emerging technology that has the potential to change human history. In this thesis, we propose to dive deep into this new technology through theory background and computational experimentation. We explain the theoretical background needed to understand how quantum computing w...
- Autores:
-
Laguna Guantiva, Mateo
- Tipo de recurso:
- Trabajo de grado de pregrado
- Fecha de publicación:
- 2022
- Institución:
- Universidad de los Andes
- Repositorio:
- Séneca: repositorio Uniandes
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.uniandes.edu.co:1992/63600
- Acceso en línea:
- http://hdl.handle.net/1992/63600
- Palabra clave:
- Quantum computing
Machine learning
AWS
Ingeniería
- Rights
- openAccess
- License
- Atribución 4.0 Internacional
| Summary: | Quantum computing is an emerging technology that has the potential to change human history. In this thesis, we propose to dive deep into this new technology through theory background and computational experimentation. We explain the theoretical background needed to understand how quantum computing works, then we use this theory background to make a computational implementation on the AWS cloud using Amazon Braket (i.e. AWS service to use quantum processors). As the result of this project, we deliver a document with the theory background required and the Python- code that implements PCA using Amazon Braket. Computational results are consistent with the theory background described in the thesis. |
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