Reinforcement Learning vs. Gradient-Based Optimisation for Robust Energy Landscape Control of Spin-1/2 Quantum Networks-Reference-Cited by-同舟云学术

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Reinforcement Learning vs. Gradient-Based Optimisation for Robust Energy Landscape Control of Spin-1/2 Quantum Networks

Published:2021-12-14 Issue: Volume: Page:
ISSN:
Container-title:2021 60th IEEE Conference on Decision and Control (CDC)
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Author:

Khalid I.¹,Weidner C. A.²,Jonckheere E. A.³,Schirmer S. G.⁴,Langbein F. C.¹

Affiliation:

1. Cardiff University,School of Computer Science and Informatics,Wales,UK

2. Aarhus University,Institute for Physics and Astronomy,Denmark

3. University of Southern California,Department of Electrical Engineering,Los Angeles,CA,90089

4. Swansea University,College of Science,Swansea,Wales,UK

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9682670/9682776/09683463.pdf?arnumber=9683463

Reference37 articles.

1. Addressing function approximation error in actor-critic methods;fujimoto;Int Conf Machine Learning,2018

2. Continuous control with deep reinforcement learning;lillicrap,2015

3. Proximal policy optimization algorithms;schulman,2017

4. Trust region policy optimization;schulman;Int Conf Machine Learning,2015

5. Combining Trust Region and Line Search Techniques

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1. Thresholds for the distributed surface code in the presence of memory decoherence;AVS Quantum Science;2024-07-01

2. Sample-efficient model-based reinforcement learning for quantum control;Physical Review Research;2023-10-02

3. Statistically characterizing robustness and fidelity of quantum controls and quantum control algorithms;Physical Review A;2023-03-14

4. Information Transfer in Spintronics Networks under Worst Case Uncertain Parameter Errors;2022 IEEE 61st Conference on Decision and Control (CDC);2022-12-06

5. Quantum optimal control in quantum technologies. Strategic report on current status, visions and goals for research in Europe;EPJ Quantum Technology;2022-07-20

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