Exploring the applications of deep reinforcement learning and quantum variational circuit In quantum machine learning
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2021.
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Brac University
2021
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| Онлайн доступ: | http://hdl.handle.net/10361/15680 |
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10361-156802022-01-26T10:20:09Z Exploring the applications of deep reinforcement learning and quantum variational circuit In quantum machine learning Saha, Prashanta Kumar Saha, Vishal Upoma, Ipshita Bonhi Rahman, Md Reshad Ur Department of Computer Science and Engineering, Brac University Quantum computing Reinforcement learning Quantum Machine Learning (QML) Quantum Variational Circuit (QVC) Deep Q-Network (DQN) Double Deep Q-Network (DDQN) OpenAI Gym IBM-Q TensorFlow Quantum Data mining Quantum theory Machine learning This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2021. Cataloged from PDF version of thesis. Includes bibliographical references (pages 30-31). In recent years, quantum computing has outperformed classical computing in many aspects, including the advancement of approaches in Reinforcement Learning prob- lems. Particularly, it has the power to utilize the quantum phenomena of super- position and entanglement, that can fastened the calculation of a vast amount of data which is very challenging for classical computers. Unfortunately, the current Quantum Computing platforms are very complex to initiate classical reinforcement learning problems for uncontrollability and intricacy of quantum circuits. In our work, we explore the application of Quantum Variational Circuit (QVC) in Deep Q- Network (DQN) instead of classical Reinforcement Learning approaches to enhance the performance of Reinforcement Learning. To achieve that, we use Quantum Vari- ational Circuit (QVC) based reinforcement learning approaches to solve the classical problems and we also solve the classical problems using classical DQN and Double Deep Q-Network (DDQN) Reinforcement Learning to compare between classical and quantum approaches. We solve Atari and Lunar Lander in OpenAI Gym envi- ronments using QVC based DQN Reinforcement learning. We study encoding tech- niques such as amplitude encoding, scaled encoding and directional encoding which were previously used in this paper[1]. We exercise IBM's open-source SDK (QISKit) and IBM-Q for quantum circuit implementation which can produce improved appli- cations like Quantum error Correction codes etc. We also use TensorFlow Quantum to implement the hybrid classical-quantum computation and experimentally analyze our work. Prashanta Kumar Saha Vishal Saha B. Computer Science 2021-12-01T05:40:08Z 2021-12-01T05:40:08Z 2021 2021-09 Thesis ID 17301103 ID 19101671 http://hdl.handle.net/10361/15680 en Brac University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. 31 pages application/pdf Brac University |
| institution |
Brac University |
| collection |
Institutional Repository |
| language |
English |
| topic |
Quantum computing Reinforcement learning Quantum Machine Learning (QML) Quantum Variational Circuit (QVC) Deep Q-Network (DQN) Double Deep Q-Network (DDQN) OpenAI Gym IBM-Q TensorFlow Quantum Data mining Quantum theory Machine learning |
| spellingShingle |
Quantum computing Reinforcement learning Quantum Machine Learning (QML) Quantum Variational Circuit (QVC) Deep Q-Network (DQN) Double Deep Q-Network (DDQN) OpenAI Gym IBM-Q TensorFlow Quantum Data mining Quantum theory Machine learning Saha, Prashanta Kumar Saha, Vishal Exploring the applications of deep reinforcement learning and quantum variational circuit In quantum machine learning |
| description |
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2021. |
| author2 |
Upoma, Ipshita Bonhi |
| author_facet |
Upoma, Ipshita Bonhi Saha, Prashanta Kumar Saha, Vishal |
| format |
Thesis |
| author |
Saha, Prashanta Kumar Saha, Vishal |
| author_sort |
Saha, Prashanta Kumar |
| title |
Exploring the applications of deep reinforcement learning and quantum variational circuit In quantum machine learning |
| title_short |
Exploring the applications of deep reinforcement learning and quantum variational circuit In quantum machine learning |
| title_full |
Exploring the applications of deep reinforcement learning and quantum variational circuit In quantum machine learning |
| title_fullStr |
Exploring the applications of deep reinforcement learning and quantum variational circuit In quantum machine learning |
| title_full_unstemmed |
Exploring the applications of deep reinforcement learning and quantum variational circuit In quantum machine learning |
| title_sort |
exploring the applications of deep reinforcement learning and quantum variational circuit in quantum machine learning |
| publisher |
Brac University |
| publishDate |
2021 |
| url |
http://hdl.handle.net/10361/15680 |
| work_keys_str_mv |
AT sahaprashantakumar exploringtheapplicationsofdeepreinforcementlearningandquantumvariationalcircuitinquantummachinelearning AT sahavishal exploringtheapplicationsofdeepreinforcementlearningandquantumvariationalcircuitinquantummachinelearning |
| _version_ |
1814309287723270144 |