research-article

A quantum system control method based on enhanced reinforcement learning

Authors:

Mohammed ZidanAuthors Info & Claims

Soft Computing, Volume 26, Issue 14

Pages 6567 - 6575

https://doi.org/10.1007/s00500-022-07179-5

Published: 01 July 2022 Publication History

Abstract

Traditional quantum system control methods often face different constraints, and are easy to cause both leakage and stochastic control errors under the condition of limited resources. Reinforcement learning has been proved as an efficient way to complete the quantum system control task. To learn a satisfactory control strategy under the condition of limited resources, a quantum system control method based on enhanced reinforcement learning (QSC-ERL) is proposed. The states and actions in reinforcement learning are mapped to quantum states and control operations in quantum systems. By using new enhanced neural networks, reinforcement learning can quickly achieve the maximization of long-term cumulative rewards, and a quantum state can be evolved accurately from an initial state to a target state. According to the number of candidate unitary operations, the three-switch control is used for simulation experiments. Compared with other methods, the QSC-ERL achieves close to 1 fidelity learning control of quantum systems, and takes fewer episodes to quantum state evolution under the condition of limited resources.

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Lak ABoostani RAlenizi FMohammed AFakhrahmad S(2024)RoBERTa, ResNeXt and BiLSTM with self-attentionApplied Soft Computing10.1016/j.asoc.2024.112018164:COnline publication date: 1-Oct-2024
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Index Terms

A quantum system control method based on enhanced reinforcement learning
1. Computing methodologies
  1. Artificial intelligence
    1. Control methods
      1. Computational control theory
    2. Search methodologies
  2. Machine learning
    1. Machine learning approaches
      1. Neural networks
2. Theory of computation

Index terms have been assigned to the content through auto-classification.

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Published In

cover image Soft Computing - A Fusion of Foundations, Methodologies and Applications

Soft Computing - A Fusion of Foundations, Methodologies and Applications Volume 26, Issue 14

Jul 2022

474 pages

ISSN:1432-7643

EISSN:1433-7479

Issue’s Table of Contents

© The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022.

Publisher

Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 01 July 2022

Accepted: 05 May 2022

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National Natural Science Foundation of China

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Cited By

Liu HLu XCheng KLiu X(2024)A Multi-Embedding Fusion Network for attributed graph clusteringApplied Soft Computing10.1016/j.asoc.2024.112073165:COnline publication date: 1-Nov-2024
https://dl.acm.org/doi/10.1016/j.asoc.2024.112073
Lak ABoostani RAlenizi FMohammed AFakhrahmad S(2024)RoBERTa, ResNeXt and BiLSTM with self-attentionApplied Soft Computing10.1016/j.asoc.2024.112018164:COnline publication date: 1-Oct-2024
https://dl.acm.org/doi/10.1016/j.asoc.2024.112018
Gu MLi DLiu JShan WLiu S(2024)A negative selection algorithm with hypercube interface detectors for anomaly detection▪Applied Soft Computing10.1016/j.asoc.2024.111339154:COnline publication date: 1-Mar-2024
https://dl.acm.org/doi/10.1016/j.asoc.2024.111339
Toscano-Miranda RAguilar JHoyos WCaro MTrebilcok AToro M(2024)Different transfer learning approaches for insect pest classification in cottonApplied Soft Computing10.1016/j.asoc.2024.111283153:COnline publication date: 1-Mar-2024
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Zheng JWang WLi ZWu Q(2023)Multi-layer double deep Q network for active distribution network equivalent modeling with internal identification for EV loads▪Applied Soft Computing10.1016/j.asoc.2023.110834147:COnline publication date: 1-Nov-2023
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