Analysis and discrimination of tectonic settings based on stacking quantum neural networks

Jiawen ZHANG, Mingchao LI, Shuai HAN, Jingyi ZHANG

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Earth Science Frontiers ›› 2024, Vol. 31 ›› Issue (3) : 511-519. DOI: 10.13745/j.esf.sf.2023.3.3

Analysis and discrimination of tectonic settings based on stacking quantum neural networks

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Abstract

Quantum geoscience represents a cutting-edge interdisciplinary field that leverages quantum computing and quantum machine learning algorithms to revolutionize the analysis of geological data. Among these advancements, the quantum neural network stands out for its efficiency and accuracy in processing complex multi-source data. This study focuses on addressing the challenge of discriminating tectonic settings, enhancing the quantum neural network (S-QNN) with an ensemble strategy to differentiate between basalt, gabbro, and spinel settings. Comparative analyses are conducted with four traditional algorithms (SVM, RF, KNN, NB), artificial neural network (ANN), and traditional quantum neural network (QNN). Results demonstrate that the S-QNN model outperforms the optimal traditional algorithm by 5.67%, 6.19%, and 13.34% in the respective cases, and surpasses the QNN by 3.11%, 4.99%, and 3.84%. The S-QNN model exhibits robustness and versatility, highlighting its superiority in data processing. This study underscores the potential of quantum machine learning algorithms in geoscience research, showcasing the advantages of S-QNN and paving the way for innovative integration of quantum science and geoscience.

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quantum geoscience / tectonic settings discrimination / rock and mineral / geochemistry / stack integration algorithm / stacking quantum neural network (S-QNN)

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Jiawen ZHANG , Mingchao LI , Shuai HAN , et al. Analysis and discrimination of tectonic settings based on stacking quantum neural networks. Earth Science Frontiers. 2024, 31(3): 511-519 https://doi.org/10.13745/j.esf.sf.2023.3.3

References

List( Publishing order | Descend order by publishing year | Descend order by cited within ) Chart analysis
[1]
张旗, 朱月琴, 焦守涛. 论传统研究与大数据研究的关系(代序)[J]. 地质通报, 2019, 38(12): 1939-1942.
本文引用 [1]
[2]
董少春, 齐浩, 胡欢. 地球科学大数据的现状与发展[J]. 科学技术与工程, 2019, 19(20): 1-11.
本文引用 [1]
[3]
罗建民, 张旗. 大数据开创地学研究新途径: 查明相关关系, 增强研究可行性[J]. 地学前缘, 2019, 26(4): 6-12.
本文引用 [1]
[4]
冯军, 张琪, 罗建民. 深度挖掘数据潜在价值提高找矿靶区定量优选精度[J]. 地学前缘, 2022, 29(4): 403-411.
本文引用 [1]
[5]
周永章, 陈烁, 张旗, 等. 大数据与数学地球科学研究进展: 大数据与数学地球科学专题代序[J]. 岩石学报, 2018, 34(2): 255-263.
本文引用 [1]
[6]
刘睿, 左蕾, 张鹏, 等. 纳米地质学: 量子科学走进地质学的桥梁[J]. 地学前缘, 2023, 30(3): 308-312.
本文引用 [1]
[7]
张旗, 焦守涛, 李明超, 等. 量子纠缠技术在地质学上应用的可能性[J]. 地学前缘, 2019, 26(4): 159-169.
本文引用 [1]
[8]
MONTANARO A. Quantum algorithms: an overview[J]. NPJ Quantum Information, 2016, 2(1): 15023.
本文引用 [1]
[9]
BIAMONTE J. WITTEK P, PANCOTTI N, et al. Quantum machine learning[J]. Nature, 2017, 549(7671): 195-202.
本文引用 [1]
[10]
CAMPBELL E T, TERHAL B M, VUILLOT C. Roads towards fault-tolerant universal quantum computation[J]. Nature, 2017, 549(7671): 172-179.
本文引用 [1]
[11]
LUO Y H, ZHONG H S, ERHARD M, et al. Quantum teleportation in high dimensions[J]. Physical Review Letters, 2019, 123(7): 070505.
本文引用 [1]
[12]
RAO K B. Computer systems architecture vs quantum computer[C]//Proceedings of International Conference on Intelligent Computing and Control Systems (ICICCS). Madurai: Vaigai College of Engineering, 2017, 8250619: 1018-1023.
本文引用 [1]
[13]
CHAMBERLAND C, NOH K, ARRANGOIZ-ARRIOLA P, et al. Building a fault-tolerant quantum computer using concatenated cat codes[J]. PRX Quantum, 2022, 3(1): 010329.
本文引用 [1]
[14]
PANET I, FLURY J, BIANCALE R, et al. Earth system mass transport mission (e.motion): a concept for future earth gravity field measurements from space[J]. Surveys in Geophysics, 2013, 34(2): 141-163.
本文引用 [1]
[15]
JANVIER C, MÉNORET V, DESRUELLE B, et al. Compact differential gravimeter at the quantum projection-noise limit[J]. Physical Review A, 2022, 105(2): 022801.
本文引用 [1]
[16]
LOPEZ CFA, LAMATA L, RETAMAL JC, et al. Multiqubit and multilevel quantum reinforcement learning with quantum technologies[J]. Plos One, 2018, 13(7): 1-25.
本文引用 [1]
[17]
REBENTROST P, MOHSENI M, LLOYD S. Quantum support vector machine for big data classification[J]. Physical Review Letters, 2014, 113(13): 130503.
本文引用 [1]
[18]
LI Z K, CHAI Z H, GUO Y H, et al. Resonant quantum principal component analysis[J]. Science Advances, 2021, 7(34): eabg2589.
本文引用 [1]
[19]
KAK S C. Quantum neural computing[M]//HAWKES P W. Advances in imaging and electron physics. Amsterdam: Elsevier, 1995: 259-313.
本文引用 [1]
[20]
王良玉, 张明林, 祝洪涛, 等. 人工神经网络及其在地学中的应用综述[J]. 世界核地质科学, 2021, 38(1): 15-26.
本文引用 [1]
[21]
吕颜轩, 高庆, 吕金虎, 等. 面向近期量子处理器的量子神经网络研究进展[J]. 中国科学: 技术科学, 2022, 52(4): 547-564.
本文引用 [1]
[22]
高骏, 何俊佳. 量子遗传神经网络在变压器油中溶解气体分析中的应用[J]. 中国电机工程学报, 2010, 30(30): 121-127.
本文引用 [2]
[23]
HUANG R, TAN X Q, XU Q S. Variational quantum tensor networks classifiers[J]. Neurocomputing, 2021, 452: 89-98.
本文引用 [2]
[24]
LLOYD S, WEEDBROOK C. Quantum generative adversarial learning[J]. Physical Review Letters, 2018, 121(4): 040502.
本文引用 [2]
[25]
WEI S J, CHEN Y H, ZHOU Z R, et al. A quantum convolutional neural network on NISQ devices[J]. AAPPS Bulletin, 2022, 32(1): 1-11.
本文引用 [2]
[26]
郭知鑫, 杨永太, 任祎, 等. 二连盆地吉尔嘎朗图凹陷南部基底花岗岩形成演化及其大地构造背景研究[J]. 地学前缘, 2023, 30(2): 259-271.
本文引用 [1]
[27]
PEARCE J A, CANN J R. Tectonic setting of basic volcanic rocks determined using trace element analyses[J]. Earth and Planetary Science Letters, 1973, 19(2): 290-300.
本文引用 [1]
[28]
李曙光. 蛇绿岩生成构造环境的Ba-Th-Nb-La判别图[J]. 岩石学报, 1993, 9(2): 146-157.
本文引用 [1]
[29]
张旗. 如何正确使用玄武岩判别图[J]. 岩石学报, 1990, 6(2): 87-94.
本文引用 [2]
[30]
LI C S, ARNDT N T, TANG Q Y, et al. Trace element indiscrimination diagrams[J]. Lithos, 2015, 232: 76-83.
本文引用 [2]
[31]
周永章, 张良均, 张澳多, 等. 地球科学大数据挖掘与机器学习[M]. 广州: 中山大学出版社, 2018.
本文引用 [2]
[32]
周永章, 左仁广, 刘刚, 等. 数学地球科学跨越发展的十年: 大数据、人工智能算法正在改变地质学[J]. 矿物岩石地球化学通报, 2021, 40(3): 556-573, 777.
本文引用 [2]
[33]
韩帅, 李明超, 刘承照, 等. 基于玄武岩大数据的大地构造环境智能挖掘判别与分析[J]. 岩石学报, 2018, 34(11): 3207-3216.
本文引用 [2]
[34]
焦守涛, 周永章, 张旗, 等. 基于GEOROC数据库的全球辉长岩大数据的大地构造环境智能判别研究[J]. 岩石学报, 2018, 34(11): 3189-3194.
本文引用 [1]
[35]
UEKI K, HINO H, KUWATANI T. Geochemical discrimination and characteristics of magmatic tectonic settings: a machine-learning-based approach[J]. Geochemistry, Geophysics, Geosystems, 2018, 19(4): 1327-1347.
本文引用 [1]
[36]
HAN S, LI M C, REN Q B. Discriminating among tectonic settings of spinel based on multiple machine learning algorithms[J]. Big Earth Data, 2019, 3(1): 67-82.
本文引用 [1]
[37]
REN Q B, LI M C, HAN S, et al. Basalt tectonic discrimination using combined machine learning approach[J]. Minerals, 2019, 9(6): 376.
本文引用 [1]
[38]
朱紫怡, 周飞, 王瑀, 等. 基于机器学习的锆石成因分类研究[J]. 地学前缘, 2022, 29(5): 464-475.
本文引用 [2]
[39]
葛粲, 汪方跃, 顾海欧, 等. 基于卷积神经网络和火山岩大数据的构造源区判别[J]. 地学前缘, 2019, 26(4): 22-32.
本文引用 [1]
[40]
JENKINS B K, TANGUAY A R. Handbook of neural computing and neural networks[M]. Boston: MIT Press, 1995.
本文引用 [1]
[41]
FREDRICK M. Artificial neural network-based decision support systems in manufacturing processes: a systematic literature review[J]. Computers and Industrial Engineering, 2022, 165: 107964.
本文引用 [1]
[42]
TAKAHASHI K, KUROKAWA M, HASHIMOTO M. Multi-layer quantum neural network controller trained by real-coded genetic algorithm[J]. Neurocomputing, 2014, 134: 159-164.
本文引用 [1]
[43]
JESWAL S K, CHAKRAVERTY S. Recent developments and applications in quantum neural network: a review[J]. Archives of Computational Methods in Engineering, 2019, 26(4): 793-807.
本文引用 [1]
[44]
KILLORAN N, BROMLEY T R, ARRAZOLA J M, et al. Continuous-variable quantum neural networks[J]. Physical Review Research, 2019, 1(3): 033063.
本文引用 [1]
[45]
DIVINCENZO D P. Quantum gates and circuits[J]. Proceedings of the Royal Society of London Series A: Mathematical, Physical and Engineering Sciences, 1998, 454(1969): 261-276.
本文引用 [1]
[46]
NORDHAUSEN K. Ensemble methods: foundations and algorithms by Zhi-Hua Zhou[J]. International Statistical Review, 2013, 81(3): 470.
本文引用 [1]
[47]
ZHANG J W, LI M C, HAN S, et al. Estimation of seismic wave incident angle using vibration response data and stacking ensemble algorithm[J]. Computers and Geotechnics, 2021, 137(5): 104255.
本文引用 [1]

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