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Machine learning for quantum control and information processing in trapped-ion systems

delete2026-01-01
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PRE
AI
L
Li, Jiachong
D
Ding, GeYi
Y
Yuan, Quan
Y
Yuan, Mingzhe *
F
Feng, Mang *
DOI：10.1360/SSPMA-2025-0278delete
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摘要

摘要

En 中文
离子阱系统是量子信息处理最重要的物理平台之一。在全球研究团队的共同努力下，离子阱架构已朝着小型化、集成化和光学可及性提升的方向发展，同时显著增强了多离子捕获和高精度控制能力。作为人工智能的核心方法，机器学习为量子系统的建模与优化提供了强大工具，并发挥着日益重要的作用。本文概述了离子阱架构的演变和离子囚禁的基本原理，并批判性地评估了传统机器学习和量子机器学习在系统级优化及离子阱平台的量子信息处理中的应用及未来前景。
Keyword:
ion trap
machine learning
quantum control
quantum information processing

期刊

S
Scientia Sinica-Physica Mechanica & Astronomica
IF:
0.5
论文数:
58
被引数:
0

机构

I
innovation academy for precision measurement science & technology, cas
学者数:
1.2K
论文数: 897
被引数: 1
C
chinese academy of sciences
学者数:
56.7W
论文数: 45.0W
被引数: 704
引用论文

引用论文

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Observation of Non-Markovian Spin Dynamics in a Jaynes-Cummings-Hubbard Model Using a Trapped-Ion Quantum Simulator
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err2022-09-28
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errB.-W. Li; Q.-X. Mei; Y.-K. Wu; M.-L. Cai; Y. Wang; L. Yao; Z.-C. Zhou; L.-M. Duan
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Quantum-nondemolition state detection and spectroscopy of single trapped molecules
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Efficient generation of spin cat states with twist-and-turn dynamics via machine optimization
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errRandall Correll; Sean J. Weinberg; Fabio Sanches; Takanori Ide; Takafumi Suzuki
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errNature
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err2022-09-07
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errB. C. Nichol; R. Srinivas; D. P. Nadlinger; P. Drmota; D. Main; G. Araneda; C. J. Ballance; D. M. Lucas
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Style-based quantum generative adversarial networks for Monte Carlo events
errQuantum
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err2022-08-17
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errCarlos Bravo-Prieto; Julien Baglio; Marco Cè; Anthony Francis; Dorota M. Grabowska; Stefano Carrazza
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