A tutorial on optimal control and reinforcement learning methods for quantum technologies

被引：0
|
作者：
Giannelli, Luigi [1 ,2 ]
Sgroi, Sofia [4 ]
Brown, Jonathon [4 ]
Paraoanu, Gheorghe Sorin [5 ]
Paternostro, Mauro [4 ]
Paladino, Elisabetta [1 ,2 ,3 ]
Falci, Giuseppe [1 ,2 ,3 ]
机构：
[1] Dipartimento di Fisica e Astronomia Ettore Majorana, Università di Catania, Via S. Sofia 64, Catania,95123, Italy
[2] CNR-IMM, UoS Università, Catania,95123, Italy
[3] INFN, Sez. Catania, Catania,95123, Italy
[4] Centre for Theoretical Atomic, Molecular, and Optical Physics, School of Mathematics and Physics, Queens University, Belfast,BT7 1NN, United Kingdom
[5] QTF Centre of Excellence, Department of Applied Physics, Aalto University School of Science, P.O. Box 15100, AALTO,FI-00076, Finland
来源：
Physics Letters, Section A: General, Atomic and Solid State Physics | 2022年 / 434卷
关键词：
103;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
引用
收藏
相关论文
共 50 条
  • [1] A tutorial on optimal control and reinforcement learning methods for quantum technologies
    Giannelli, Luigi
    Sgroi, Sofia
    Brown, Jonathon
    Paraoanu, Gheorghe Sorin
    Paternostro, Mauro
    Paladino, Elisabetta
    Falci, Giuseppe
    PHYSICS LETTERS A, 2022, 434
  • [2] Quantum optimal control of multilevel dissipative quantum systems with reinforcement learning
    An, Zheng
    Song, Hai-Jing
    He, Qi-Kai
    Zhou, D. L.
    PHYSICAL REVIEW A, 2021, 103 (01)
  • [3] A tutorial on pseudospectral methods for computational optimal control
    Becerra V.M.
    Galvão R.K.H.
    Controle y Automacao, 2010, 21 (03): : 224 - 244
  • [4] Multiqubit and multilevel quantum reinforcement learning with quantum technologies
    Cardenas-Lopez, F. A.
    Lamata, L.
    Retamal, J. C.
    Solano, E.
    PLOS ONE, 2018, 13 (07):
  • [5] Optimal Greedy Control in Reinforcement Learning
    Gorobtsov, Alexander
    Sychev, Oleg
    Orlova, Yulia
    Smirnov, Evgeniy
    Grigoreva, Olga
    Bochkin, Alexander
    Andreeva, Marina
    SENSORS, 2022, 22 (22)
  • [6] Reinforcement Learning Informed by Optimal Control
    Onnheim, Magnus
    Andersson, Pontus
    Gustavsson, Emil
    Jirstrand, Mats
    ARTIFICIAL NEURAL NETWORKS AND MACHINE LEARNING - ICANN 2019: WORKSHOP AND SPECIAL SESSIONS, 2019, 11731 : 403 - 407
  • [7] A TUTORIAL SURVEY OF REINFORCEMENT LEARNING
    KEERTHI, SS
    RAVINDRAN, B
    SADHANA-ACADEMY PROCEEDINGS IN ENGINEERING SCIENCES, 1994, 19 : 851 - 889
  • [8] A tutorial introduction to reinforcement learning
    Vidyasagar, Mathukumalli
    SICE JOURNAL OF CONTROL MEASUREMENT AND SYSTEM INTEGRATION, 2023, 16 (01) : 172 - 191
  • [9] Designing fast quantum gates using optimal control with a reinforcement-learning ansatz
    Sarma, Bijita
    Hartmann, Michael J.
    PHYSICAL REVIEW APPLIED, 2025, 23 (01):
  • [10] Reinforcement Learning for Quantum Metrology via Quantum Control
    Vedaie, Seyed Shakib
    Palittapongarnpim, Pantita
    Sanders, Barry C.
    2018 IEEE PHOTONICS SOCIETY SUMMER TOPICAL MEETING SERIES (SUM), 2018, : 163 - 164
12345