Model-Free Quantum Gate Design and Calibration Using Deep Reinforcement Learning

Author:

Shindi Omar1ORCID,Yu Qi1ORCID,Girdhar Parth1ORCID,Dong Daoyi1ORCID

Affiliation:

1. School of Engineering and Information Technology, University of New South Wales, Canberra, ACT, Australia

Funder

Australian Research Council’s Future Fellowship

U. S. Office of Naval Research Global

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computer Science Applications,Artificial Intelligence

Cited by 5 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Multiagent Hierarchical Deep Reinforcement Learning for Operation Optimization of Grid-Interactive Efficient Commercial Buildings;IEEE Transactions on Artificial Intelligence;2024-08

2. Reinforcement learning pulses for transmon qubit entangling gates;Machine Learning: Science and Technology;2024-06-01

3. Learning-Based Quantum Control for Optimal Pure State Manipulation;IEEE Control Systems Letters;2024

4. Guided Reward Design in Continuous Reinforcement Learning for Quantum Control;2023 IEEE International Conference on Systems, Man, and Cybernetics (SMC);2023-10-01

5. Fast Quantum Gate Design with Deep Reinforcement Learning Using Real-Time Feedback on Readout Signals;2023 IEEE International Conference on Quantum Computing and Engineering (QCE);2023-09-17

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