Quantum machine learning
We use reinforcement learning techniques to optimize the Quantum Approximate Optimization Algorithm when applied to the MaxCut problem. We explore Q-learning based techniques both for continuous and discrete action environments with regular and irregular graphs.
| Autor: | |
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| Tipo de recurso: | tesis de maestría |
| Fecha de publicación: | 2019 |
| País: | España |
| Institución: | Universitat Politècnica de Catalunya (UPC) |
| Repositorio: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglés |
| OAI Identifier: | oai:upcommons.upc.edu:2117/133060 |
| Acceso en línea: | https://hdl.handle.net/2117/133060 |
| Access Level: | acceso abierto |
| Palabra clave: | Machine learning Quantum computers Aprenentatge per reforç Computació Quàntica Quantum Approximate Optimization Algorithm Deep Q-Learning Màxim Tall. Reinforcement Learning Quantum Computing Aprenentatge automàtic Ordinadors quàntics Àrees temàtiques de la UPC::Informàtica |
| Sumario: | We use reinforcement learning techniques to optimize the Quantum Approximate Optimization Algorithm when applied to the MaxCut problem. We explore Q-learning based techniques both for continuous and discrete action environments with regular and irregular graphs. |
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