Active learning algorithm applied in quantum physics

YAO Juan

doi:10.12405/j.issn.2097-1486.2023.01.006

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Active learning algorithm applied in quantum physics

更新时间：2024-03-29

- Active learning algorithm applied in quantum physics
- Emerging Science and Technology(Nat.Sci.Ed.) Vol. 2, Issue 1, Pages: 41-48(2023)
- 作者机构：
  
  1.南方科技大学量子科学与工程研究院，广东深圳 518055
  2.国际量子研究院，广东深圳 518048
  3.广东省量子科学与工程重点实验室，南方科技大学，广东深圳 518055
- 作者简介：
- 基金信息：
- DOI：10.12405/j.issn.2097-1486.2023.01.006
  CLC： TP181
- Published：25 March 2023，
  
  Received：20 January 2023，
  
  Revised：25 February 2023，
扫描看全文
姚娟.主动学习算法在量子物理中的应用[J].新兴科学和技术趋势,2023,2(1):41-48.

YAO Juan.Active learning algorithm applied in quantum physics[J].Emerging Science and Technology,2023,2(1):41-48.
姚娟.主动学习算法在量子物理中的应用[J].新兴科学和技术趋势,2023,2(1):41-48. DOI： 10.12405/j.issn.2097-1486.2023.01.006.

YAO Juan.Active learning algorithm applied in quantum physics[J].Emerging Science and Technology,2023,2(1):41-48. DOI： 10.12405/j.issn.2097-1486.2023.01.006.

摘要

主动学习方法作为一种能够自主选择数据样本的机器学习算法，在处理量子物理问题中有着诸多应用。通过“Query by committee（QBC）”的主动学习策略对训练数据进行动态扩充，从而在样本数据较少的情况下，可以有效提高训练模型的表现性能。针对量子物理中数据获取困难的特征，使用主动学习算法优化数据组成结构，为模型的训练提供有效信息，提高模型训练表现性能。通过介绍在多维函数拟合以及优化两类量子物理问题中应用实例，体现主动学习算法在处理此类量子物理问题的可行性和优越性。

Abstract

Active learning method， as a machine learning algorithm which can automatically selects data samples， has many applications in solving quantum physics problems. The “query by committee” strategy adopted by active learning method can extend the training dataset dynamically. It improves the performance of the training model effectively even with a small training dataset. In reaction to the difficulty of obtaining data points in quantum physics， active learning algorithm optimizes the structure of the training dataset， providing efficient information for model training. The application in quantum physics of multidimensional function fitting and optimization demonstrated the feasibility and superiority of active learning algorithms in solving quantum physics problems.

关键词

主动学习算法量子物理拟合优化

Keywords

active learning algorithmquantum physicsfitting and optimization

references

SETTLES B. Active Learning［M/OL］. Cham： Springer International Publishing， 2012［2023-08-29］. https：//link.springer.com/10.1007/978-3-031-01560-1https://link.springer.com/10.1007/978-3-031-01560-1.

YAO J，WU Y，KOO J，et al. Active learning algorithm for computational physics［J］. Physical Review Research，2020，2（1）：013287. DOI：10.1103/PhysRevResearch.2.013287http://dx.doi.org/10.1103/PhysRevResearch.2.013287.

LANGE H，KEBRIČ M，BUSER M，et al. Adaptive Quantum State Tomography with Active Learning［M/OL］. arXiv，2023［2023-08-29］. http：//arxiv.org/abs/2203.15719http://arxiv.org/abs/2203.15719.

QIU Y，ZHUANG M，HUANG J，et al. Bayesian Phase Estimation via Active Learning［M/OL］. arXiv，2021［2023-08-29］. http：//arxiv.org/abs/2107.00196http://arxiv.org/abs/2107.00196.

DING Y，MARTÍN-GUERRERO J D，SANZ M，et al. Retrieving Quantum Information with Active Learning［J］. Physical Review Letters，2020，124（14）：140504. DOI：10.1103/PhysRevLett.124.140504http://dx.doi.org/10.1103/PhysRevLett.124.140504.

DING Y，MARTÍN-GUERRERO J D，SONG Y，et al. Active learning for the optimal design of multinomial classification in physics［J］. Physical Review Research，2022，4（1）：013213. DOI：10.1103/PhysRevResearch.4.013213http://dx.doi.org/10.1103/PhysRevResearch.4.013213.

DUTT A，PEDNAULT E，WU C W，et al. Active learning of quantum system Hamiltonians yields query advantage［J/OL］. Physical Review Research，2023，5（3）： 033060［2023-08-29］. https：//link.aps.org/doi/10.1103/PhysRevResearch.5.033060https://link.aps.org/doi/10.1103/PhysRevResearch.5.033060.

ZHU R，PIKE-BURKE C，MINTERT F. Active Learning for Quantum Mechanical Measurements［J/OL］. 2022［2023-08-29］. https：//arxiv.org/abs/2212.07513https://arxiv.org/abs/2212.07513.

DING C，XU X Y，NIU Y F，et al. Active Learning on a Programmable Photonic Quantum Processor［J/OL］. 2022［2023-08-29］. https：//arxiv.org/abs/2208.02104https://arxiv.org/abs/2208.02104.

WU Y，MENG Z，WEN K，et al. Active Learning Approach to Optimization of Experimental Control*［J］. Chinese Physics Letters，2020，37（10）：103201. DOI：10.1088/0256-307X/37/10/103201http://dx.doi.org/10.1088/0256-307X/37/10/103201.

CHEN L，LIANG X，ZHAI H. The Bayesian Committee Approach for Computational Physics Problems［J/OL］. 2020［2023-08-29］. https：//arxiv.org/abs/2011.06086https://arxiv.org/abs/2011.06086.

HE Z， LI L， ZHENG S， et al. Quantum speedup for pool-based active learning［J］. Quantum Information Processing，2019，18（11）：345. DOI：10.1007/s11128-019-2460-xhttp://dx.doi.org/10.1007/s11128-019-2460-x.

CASARES P A M，MARTIN-DELGADO M A. A quantum active learning algorithm for sampling against adversarial attacks［J］. New Journal of Physics，2020，22（7）：073026. DOI：10.1088/1367-2630/ab976fhttp://dx.doi.org/10.1088/1367-2630/ab976f.

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