Advanced Quantum Control with Ensemble Reinforcement Learning: A Case Study on the XY Spin Chain
<p dir="ltr">This research presents an ensemble Reinforcement Learning (RL) approach that combines Deep Q-Network (DQN) and Proximal Policy Optimization (PPO) algorithms to tackle quantum control problems. This research aims to use the complementary strengths of DQN and PPO algorithm...
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| مؤلفون آخرون: | , , , |
| منشور في: |
2025
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| _version_ | 1864513534235246592 |
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| author | Farshad Rahimi Ghashghaei (20880995) |
| author2 | Nebrase Elmrabit (1250247) Ayyaz-Ul-Haq Qureshi (20880998) Adnan Akhunzada (20151648) Mehdi Yousefi (12201526) |
| author2_role | author author author author |
| author_facet | Farshad Rahimi Ghashghaei (20880995) Nebrase Elmrabit (1250247) Ayyaz-Ul-Haq Qureshi (20880998) Adnan Akhunzada (20151648) Mehdi Yousefi (12201526) |
| author_role | author |
| dc.creator.none.fl_str_mv | Farshad Rahimi Ghashghaei (20880995) Nebrase Elmrabit (1250247) Ayyaz-Ul-Haq Qureshi (20880998) Adnan Akhunzada (20151648) Mehdi Yousefi (12201526) |
| dc.date.none.fl_str_mv | 2025-03-14T06:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1109/ACCESS.2025.3551232 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Advanced_Quantum_Control_With_Ensemble_Reinforcement_Learning_A_Case_Study_on_the_XY_Spin_Chain/28599455 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Engineering Aerospace engineering Control engineering, mechatronics and robotics Information and computing sciences Artificial intelligence Data management and data science Machine learning Theory of computation Quantum system Ensemble learning Reinforcement learning Quantum computing Optimal control Robust control Noise Computational modeling Aerospace electronics Adaptive control |
| dc.title.none.fl_str_mv | Advanced Quantum Control with Ensemble Reinforcement Learning: A Case Study on the XY Spin Chain |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">This research presents an ensemble Reinforcement Learning (RL) approach that combines Deep Q-Network (DQN) and Proximal Policy Optimization (PPO) algorithms to tackle quantum control problems. This research aims to use the complementary strengths of DQN and PPO algorithms to develop robust and adaptive control policies for noisy and uncertain quantum systems. We comprehensively analyse the proposed ensemble learning, including algorithmic details, implementation specifics, and experimental results. Through extensive experimentation and evaluation, we demonstrate the effectiveness of the ensemble approach in learning control strategies for manipulating quantum systems towards a random target state. The results highlight the potential of ensemble RL techniques in addressing the challenges of quantum control tasks, such as system noise and dynamics. By integrating multiple RL agents within an ensemble framework, we aim to advance current developments in quantum control and create a new path for the development of adaptive control systems for quantum systems. The performance of the ensemble model is assessed against Gradient Ascent Pulse Engineering (GRAPE) and robust Model Predictive Control (MPC) to demonstrate its efficiency in highly challenging and noisy environments.</p><h2>Other Information</h2><p dir="ltr">Published in: IEEE Access<br>License: <a href="https://creativecommons.org/licenses/by/4.0" target="_blank">https://creativecommons.org/licenses/by/4.0</a><br>See article on publisher's website: <a href="https://doi.org/10.1109/access.2025.3551232" target="_blank">https://doi.org/10.1109/access.2025.3551232</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_c9e3795ec0ac471423ea69676812fa46 |
| identifier_str_mv | 10.1109/ACCESS.2025.3551232 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/28599455 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Advanced Quantum Control with Ensemble Reinforcement Learning: A Case Study on the XY Spin ChainFarshad Rahimi Ghashghaei (20880995)Nebrase Elmrabit (1250247)Ayyaz-Ul-Haq Qureshi (20880998)Adnan Akhunzada (20151648)Mehdi Yousefi (12201526)EngineeringAerospace engineeringControl engineering, mechatronics and roboticsInformation and computing sciencesArtificial intelligenceData management and data scienceMachine learningTheory of computationQuantum systemEnsemble learningReinforcement learningQuantum computingOptimal controlRobust controlNoiseComputational modelingAerospace electronicsAdaptive control<p dir="ltr">This research presents an ensemble Reinforcement Learning (RL) approach that combines Deep Q-Network (DQN) and Proximal Policy Optimization (PPO) algorithms to tackle quantum control problems. This research aims to use the complementary strengths of DQN and PPO algorithms to develop robust and adaptive control policies for noisy and uncertain quantum systems. We comprehensively analyse the proposed ensemble learning, including algorithmic details, implementation specifics, and experimental results. Through extensive experimentation and evaluation, we demonstrate the effectiveness of the ensemble approach in learning control strategies for manipulating quantum systems towards a random target state. The results highlight the potential of ensemble RL techniques in addressing the challenges of quantum control tasks, such as system noise and dynamics. By integrating multiple RL agents within an ensemble framework, we aim to advance current developments in quantum control and create a new path for the development of adaptive control systems for quantum systems. The performance of the ensemble model is assessed against Gradient Ascent Pulse Engineering (GRAPE) and robust Model Predictive Control (MPC) to demonstrate its efficiency in highly challenging and noisy environments.</p><h2>Other Information</h2><p dir="ltr">Published in: IEEE Access<br>License: <a href="https://creativecommons.org/licenses/by/4.0" target="_blank">https://creativecommons.org/licenses/by/4.0</a><br>See article on publisher's website: <a href="https://doi.org/10.1109/access.2025.3551232" target="_blank">https://doi.org/10.1109/access.2025.3551232</a></p>2025-03-14T06:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1109/ACCESS.2025.3551232https://figshare.com/articles/journal_contribution/Advanced_Quantum_Control_With_Ensemble_Reinforcement_Learning_A_Case_Study_on_the_XY_Spin_Chain/28599455CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/285994552025-03-14T06:00:00Z |
| spellingShingle | Advanced Quantum Control with Ensemble Reinforcement Learning: A Case Study on the XY Spin Chain Farshad Rahimi Ghashghaei (20880995) Engineering Aerospace engineering Control engineering, mechatronics and robotics Information and computing sciences Artificial intelligence Data management and data science Machine learning Theory of computation Quantum system Ensemble learning Reinforcement learning Quantum computing Optimal control Robust control Noise Computational modeling Aerospace electronics Adaptive control |
| status_str | publishedVersion |
| title | Advanced Quantum Control with Ensemble Reinforcement Learning: A Case Study on the XY Spin Chain |
| title_full | Advanced Quantum Control with Ensemble Reinforcement Learning: A Case Study on the XY Spin Chain |
| title_fullStr | Advanced Quantum Control with Ensemble Reinforcement Learning: A Case Study on the XY Spin Chain |
| title_full_unstemmed | Advanced Quantum Control with Ensemble Reinforcement Learning: A Case Study on the XY Spin Chain |
| title_short | Advanced Quantum Control with Ensemble Reinforcement Learning: A Case Study on the XY Spin Chain |
| title_sort | Advanced Quantum Control with Ensemble Reinforcement Learning: A Case Study on the XY Spin Chain |
| topic | Engineering Aerospace engineering Control engineering, mechatronics and robotics Information and computing sciences Artificial intelligence Data management and data science Machine learning Theory of computation Quantum system Ensemble learning Reinforcement learning Quantum computing Optimal control Robust control Noise Computational modeling Aerospace electronics Adaptive control |