基于数据挖掘的前沿技术识别方法与实证研究

doi:10.12305/j.issn.1001-506X.2024.09.20

Abstract

Abstract:

Based on the forward-looking, pioneering, exploratory, and disruptive characteristics of frontier technologies, a method of frontier technology identification based on data mining is constructed to provide support for efficient formulation of technology development strategies and rational adjustment of technology planning and layout. Firstly, data is collected and processed from the engineering index (EI) and Derwent innovation (DI) platform databases as a data source by formulating retrieval expressions. Secondly, the index system representing the characteristics of frontier technology is established, and the selected technical text data is mined to identify the frontier technology topics. Finally, empirical research is conducted in the field of quantum computing as an example, identifying frontier technology topics such as quantum error correction and optical quantum chips. This verification is confirmed by expert review and other methods, making identifying issues scientifically and practically applicable.

Key words: data mining, technology identification, frontier technology

CLC Number:

F204

Hong MIAO, Jiaxin LIAN, Weiwei LI, Guotong GENG, Haotong WANG, Huizhao ZHANG, Feifei WU. Frontier technology identification method and empirical research based on data mining[J]. Systems Engineering and Electronics, 2024, 46(9): 3082-3092.

Figures/Tables 6

Fig.1

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References 79

1	中国政府网. 国家中长期科学和技术发展规划纲要[EB/OL]. [2023-08-23]. http://www.gov.cn/jrzg/2006-02/09/content_183787.htm.
	CHINESE GOVERNMENT WEBSITE. National Medium and Long TermScience and Technology Development Plan[EB/OL]. [2023-08-23]. http://www.gov.cn/jrzg/2006-02/09/content_183787.htm.
2	XIE T , XU Y , LI Y . Nonlinear relationship between natural resources and economic growth: the role of frontier technology[J]. Resources Policy, 2024, 90, 104831. doi: 10.1016/j.resourpol.2024.104831
3	LIU H , WANG S Z , WANG J L , et al. Supercritical hydrothermal synthesis of copper nanoparticles: experimental and kinetic study[J]. Colloids and Surfaces A: Physicochemical and Engineering Aspects, 2024, 680, 132670. doi: 10.1016/j.colsurfa.2023.132670
4	HUANG X G, YU J B, CUI Y B, et al. Overview of artificial intelligence frontier technology and data retrieval application research in power industry[C]//Proc. of the 19th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery, 2023.
5	XU S , HAO L Y , YANG G C , et al. A topic models based framework for detecting and forecasting emerging technologies[J]. Technological Forecasting and Social Change, 2021, 162, 120366. doi: 10.1016/j.techfore.2020.120366
6	KIM G , BAE J . A novel approach to forecast promising technology through patent analysis[J]. Technological Forecasting and Social Change, 2017, 117, 228- 237. doi: 10.1016/j.techfore.2016.11.023
7	Science. 125 questions: exploration and dis-covery[EB/OL]. [2023-09-18]. http://www.science.org/content/resource/125-questions-exploration-and-discovery.
8	PRABHAA S S , BINDU N , MANOJ P , et al. Citation network analysis of plastic electronics: tracing the evolution and emerging research fronts[J]. Materials Today Proceedings, 2020, 33 (2): 1345- 1350.
9	AHMED A , AZAM A , BHUTTA M M A , et al. Discovering the technology evolution pathways for 3Dprinting (3DP) using bibliometric investigation and emerging applications of 3DP during COVID-19[J]. Cleaner Environmental Systems, 2021, 3, 100042. doi: 10.1016/j.cesys.2021.100042
10	REHMAN A , XING H L , KHAN M A , et al. Emerging technologies for COVID (ET-CoV) detection and diagnosis: recent advancements, applications, challenges, and future perspectives[J]. Biomedical Signal Processing and Control, 2023, 83, 104642. doi: 10.1016/j.bspc.2023.104642
80	BONETTI J , LINALE N , GROSZ D F . Heralded single-photon sources based on 2D-decorated nanowires[J]. Physics Letters A, 2022, 432, 128018. doi: 10.1016/j.physleta.2022.128018
81	ZHOU X C , ZHANG Z H , GUO W L . Dislocations as single photon sources in two-dimensional semiconductors[J]. Nano Letters, 2020, 20 (6): 4136- 4143. doi: 10.1021/acs.nanolett.9b05305
82	YE Z C , ZHAO P , TWAYANA K , et al. Overcoming the quantum limit of optical amplification in monolithic waveguides[J]. Science Advances, 2021, 7 (38) doi: 10.1126/sciadv.abi8150
83	SHAIB A , NAIM M H , FOUDA M E , et al. Efficient noise mitigation technique for quantum computing[J]. Scientific Reports, 2023, 13 (1): 3912. doi: 10.1038/s41598-023-30510-5
84	XU X Y , REN G H , FELEPPA T , et al. Self-calibrating programmable photonic integrated circuits[J]. Nature Photonics, 2022, 16 (8): 595- 602. doi: 10.1038/s41566-022-01020-z
11	CHOI Y , PARK S , LEE S . Identifying emerging technologies to envision a future innovation ecosystem: a machine learning approach to patent data[J]. Scientometrics, 2021, 126, 5431- 5476. doi: 10.1007/s11192-021-04001-1
12	GARECHANA G , RIO-BELVER R , BILDOSOLA I , et al. A method for the detection and characterization of technology fronts: analysis of the dynamics of technological change in 3D printing technology[J]. Plos One, 2019, 14 (1): e02104411.
13	PARK I , YOON B . Identifying promising research frontiers of pattern recognition through bibliometric analysis[J]. Sustainability, 2018, 10 (11): 4055. doi: 10.3390/su10114055
14	MIAARI A A , ALI H M . Batteries temperature prediction and thermal management using machine learning: an overview[J]. Energy Reports, 2023, 10, 2277- 2305. doi: 10.1016/j.egyr.2023.08.043
15	李欣, 温阳, 黄鲁成, 等. 一种基于机器学习的研究前沿识别方法研究[J]. 科研管理, 2021, 42 (1): 20- 32.
	LI X , WEN Y , HUANG L C , et al. A study of the research front identification method based on machine learning[J]. Science Research Management, 2021, 42 (1): 20- 32.
16	袁建霞, 冷伏海, 黄龙光, 等. 科技前沿方向的情报监测分析与综合研判方法探讨[J]. 图书情报工作, 2022, 66 (19): 92- 98.
	YUAN J X , LENG F H , HUANG L G , et al. Exploration of intelligence monitoring, analysis and comprehensive study and judgment methods for S&T frontier direction[J]. Library and Information Service, 2022, 66 (19): 92- 98.
17	PUCCETTI G , GIORDANO V , SPADA I , et al. Technology identification from patent texts: a novel named entity recognition method[J]. Technological Forecasting and Social Change, 2023, 186, 122160. doi: 10.1016/j.techfore.2022.122160
18	周波, 冷伏海. 技术识别研究进展[J]. 情报学进展, 2022, 1, 315- 348.
	ZHOU B , LENG F H . The research progress of technology identification[J]. Advances in Information Science, 2022, 1, 315- 348.
19	潘教峰, 王海霞, 冷伏海, 等. 《2022研究前沿》——11个大学科领域发展趋势与重点研究问题[J]. 中国科学院院刊, 2023, 38 (1): 154- 166.
	PAN J F , WANG H X , LENG F H , et al. 2022 Research fronts: development trends and key research questions in 11 broad research areas[J]. Bulletin of Chinese Academy of Sciences, 2023, 38 (1): 154- 166.
20	YOUNG O R . Effectiveness of international environmental regimes: existing knowledge, cutting-edge themes, and research strategies[J]. Proceedings of the National Academy of Sciences, 2011, 108 (50): 19853- 19860. doi: 10.1073/pnas.1111690108
21	张春博, 丁堃, 刘则渊. 大型工业实验室基础研究特征及启示——基于IBM实验室的案例研究[J]. 科技进步与对策, 2014, 31 (13): 1- 6. doi: 10.6049/kjjbydc.2013090514
	ZHANG C B , DING K , LIU Z Y . Characteristics and inspiration of basic research in large industrial laboratories—a case study based on IBM labs[J]. Science & Technology Progress and Policy, 2014, 31 (13): 1- 6. doi: 10.6049/kjjbydc.2013090514
22	李佳. 我国创业教育研究热点、前沿及其演进探析——以国家社会科学基金和全国教育科学规划资助项目文献为例[J]. 创新与创业教育, 2023, 14 (2): 21- 31. doi: 10.3969/j.issn.1674-893X.2023.02.004
	LI J . On hot topics, frontiers and evolutions of entrepreneurship education in China-based on research literature funded by the national social science fund and the national education science planning project[J]. Innovation and Entrepreneurship Education, 2023, 14 (2): 21- 31. doi: 10.3969/j.issn.1674-893X.2023.02.004
23	宋凯, 朱彦君. 专利前沿技术主题识别及趋势预测方法——以人工智能领域为例[J]. 情报杂志, 2021, 40 (1): 33- 38. doi: 10.3969/j.issn.1002-1965.2021.01.005
	SONG K , ZHU Y J . Patent frontier technology topic identification and trend prediction: a case analysis of artificial intelligence[J]. Journal of Information, 2021, 40 (1): 33- 38. doi: 10.3969/j.issn.1002-1965.2021.01.005
24	KONG Y X, HUANG B Z, WANG Y J, et al. Study on the identification of disruptive technology, evidence from Nano science[C]//Proc. of the 11th International Conference on Distributed, Ambient and Pervasive Interactions, 2023: 76-90.
25	HE Y B , LIN T , ZHANG S . Does complementary technology within an ecosystem affect disruptive innovation? Evidence from chinese electric vehicle listed firms[J]. Technology in Society, 2023, 74, 102330. doi: 10.1016/j.techsoc.2023.102330
26	HOPSTER J . What are socially disruptive technologies?[J]. Technology in Society, 2021, 67, 101750. doi: 10.1016/j.techsoc.2021.101750
27	XU X M, LI J C, JIANG J, et al. A disruptive technology identification method based on multisource data: take unmanned aerial vehicle systems as an example[C]//Proc. of the 7th International Conference on Big Data and Information Analytics, 2021: 428-435.
28	LYU L C, WANG X Z, CHEN W, et al. The research on disruptive technology identification based on scientific and technological information mining and expert consultation: a case study on the energy field[C]//Proc. of the 2nd International Conference on Artificial Intelligence in China, 2021: 469-482.
29	CHENG Y , HUANG L C , RAMLOGAN R , et al. Forecasting of potential impacts of disruptive technology in promising technological areas: elaborating the SIRS epidemic model in RFID technology[J]. Technological Forecasting and Social Change, 2017, 117, 170- 183. doi: 10.1016/j.techfore.2016.12.003
30	RONZHYN A, WIMMER M A, SPITZER V, et al. Using disruptive technologies in government: identification of research and training needs[C]//Proc. of the International Conference on Electronic Government, 2019: 276-287.
31	JIA W F , WANG S , XIE Y P , et al. Disruptive technology identification of intelligent logistics robots in AIoT industry: based on attributes and functions analysis[J]. Systems Research and Behavioral Science, 2022, 39 (3): 557- 568. doi: 10.1002/sres.2859
32	MORAVEC J W , MARTLNEZ-BRAVO M C . Global trends in disruptive technological change: social and policy implications for education[J]. On the Horizon, 2023, 31 (34): 147- 173.
33	JIA W F , XIE Y P , ZHAO Y N , et al. Research on disruptive technology recognition of China's electronic information and communication industry based on patent influence[J]. Journal of Global Information Management, 2021, 29 (2): 148- 165. doi: 10.4018/JGIM.2021030108
34	LI X, MA X D. Early identification of potential disruptive technologies using machine learning and text mining[C]//Proc. of the Portland International Conference on Management of Engineering and Technology, 2023.
35	ZHENG L X , WANG W J . Evaluation indicators of space frontier technology based on D-ANP[J]. Journal of Physics: Conference Series, 2023, 2569 (1): 012045. doi: 10.1088/1742-6596/2569/1/012045
36	李琳琳, 路云飞, 张壮, 等. 基于信息优势的指控系统指标体系构建及建模[J]. 系统工程与电子技术, 2018, 40 (3): 577- 582. doi: 10.3969/j.issn.1001-506X.2018.03.14
	LI L L , LU Y F , ZHANG Z , et al. System construction and modeling of command and control system index based on information superiority[J]. Systems Engineering and Electronics, 2018, 40 (3): 577- 582. doi: 10.3969/j.issn.1001-506X.2018.03.14
37	高楠, 彭鼎原, 傅俊英, 等. 基于专利IPC分类与文本信息的前沿技术演进分析——以人工智能领域为例[J]. 情报理论与实践, 2020, 43 (4): 123- 129.
	GAO N , PENG D Y , FU J Y , et al. Research on technology fronts prediction based on patent IPC classification and text information—taking the field of artificial intelligence as an example[J]. Information Studies: Theory & Application, 2020, 43 (4): 123- 129.
38	LIAO H C , TANG M , LI Z M , et al. Bibliometric analysis for highly cited papers in operations research and management science from 2008 to 2017 based on Essential Science Indicators[J]. Omega, 2019, 88, 223- 236. doi: 10.1016/j.omega.2018.11.005
39	JI B , ZHAO Y Q . Trend and status of the NSFC funded projects for constructed wetlands in the last decade in China[J]. Ecological Engineering, 2023, 194, 107057. doi: 10.1016/j.ecoleng.2023.107057
40	黄鲁成, 成雨, 吴菲菲, 等. 技术预测与技术预见及其客观分析方法[J]. 创新与创业管理, 2013, 1, 119- 132.
	HUANG L C , CHENG Y , WU F F , et al. Technology forecasting, technology foresight and objective analysis method[J]. Management of Innovation and Entrepreneurship, 2013, 1, 119- 132.
41	WANG H Y , SUN B , WANG P . Dominant technology identification model based on patent information toward sustainable energy development[J]. IEEE Access, 2019, 7, 141374- 141385. doi: 10.1109/ACCESS.2019.2939381
42	HO Y S . Classic articles on social work field in Social Science Citation Index: a bibliometric analysis[J]. Scientometrics, 2014, 98 (1): 137- 155. doi: 10.1007/s11192-013-1014-8
43	LEVITT J M , THELWALL M . The most highly cited library and information science articles: interdisciplinarity, first authors and citation patterns[J]. Scientometrics, 2009, 78 (1): 45- 67. doi: 10.1007/s11192-007-1927-1
44	VERHOEVEN D , BAKKER J , VEUGELERS R . Measuring technological novelty with patent-based indicators[J]. Research Policy, 2016, 45 (3): 707- 723. doi: 10.1016/j.respol.2015.11.010
45	黄鲁成, 蒋林杉, 吴菲菲. 萌芽期颠覆性技术识别研究[J]. 科技进步与对策, 2019, 36 (1): 10- 17.
	HUANG L C , JIANG L S , WU F F . The identification of disruptive technology on emerging stage[J]. Science & Technology Progress and Policy, 2019, 36 (1): 10- 17.
46	Committee on forecasting future disruptive technologies(CFFDT). Persistent forecasting of disruptive technologies[EB/OL]. [2023-09-18]. http://xueshu.baidu.com/usercenter/paper/show?paperid=ae7f0c3f4d489fe8581bc4f94353b467&-site=xueshu_se.
47	EGLI F, JOHNSTONE N, MENON C. Identifying and inducing breakthrough inventions: an application related to climate change mitigation[R]. Paris: Organisation for Economic Cooperation and Development, 2015.
48	JIANG M. Very large language model as a unified methodology of text mining[EB/OL]. [2023-08-03]. http://doi.org/10.48550/arXiv.2212.09271.
49	CHOWDHURY S , ALZARRAD A . Applications of text mining in the transportation infrastructure sector: a review[J]. Information, 2023, 14 (4): 201. doi: 10.3390/info14040201
50	RASOOL U R , AHMAD H F , RAFIQUE W , et al. Quantum computing for healthcare: a review[J]. Future Internet, 2023, 15 (3): 94. doi: 10.3390/fi15030094
51	LU Y , SIGOV A , RATKIN L , et al. Quantum computing and industrial information integration: a review[J]. Journal of Industrial Information Integration, 2023, 35, 100511. doi: 10.1016/j.jii.2023.100511
52	LOKE S W. From distributed quantum computing to quantum internet computing: an overview[EB/OL]. [2023-08-03]. http://doi.org/10.48550/arXiv.2208.10127.
53	STOOΒ V , ULMKE M , GOVAERS F . Quantum computing for applications in data fusion[J]. IEEE Trans. on Aerospace and Electronic Systems, 2023, 59 (2): 2002- 2012.
54	JERBI D. Revolutionizing computing: a comprehensive introduction to quantum computing[EB/OL]. [2023-08-03]. http://doi.org/10.36227/techrxiv.22710361.v1.
55	GUAN X, FAN J, BIAN Y B, et al. An efficient numerical method for solving the thermodynamic model of silver powder heat exchanger and its applications in the development of dilution refrigerator[EB/OL]. [2023-08-03]. http://ssrn.com/abstract=4691032.
56	ZHAI Y J , WU S G , MA D , et al. Development of a cryogen-free dilution refrigerator for superconducting quantum computing[J]. IEEE Trans. on Applied Superconductivity, 2024, 34 (3): 1700105.
57	ZHENG M W, LI J G, GUO H W, et al. Dry dilution refrige-rator driven byhigh-power 3he linear compressor[EB/OL]. [2023-08-03]. http://ssrn.com/abstract=4596026.
58	LI S , XUE J , CHEN T , et al. High-fidelity geometric quantum gates with short paths on superconducting circuits[J]. Advanced Quantum Technologies, 2021, 4 (5): 2000140. doi: 10.1002/qute.202000140
59	ZHU D Q , JAAKO T , HE Q Y , et al. Quantum computing with superconducting circuits in the picosecond regime[J]. Physical Review Applied, 2021, 16, 014024. doi: 10.1103/PhysRevApplied.16.014024
60	SUBRAMANIAN M , LUPASCU A . Efficient two-qutrit gates in superconducting circuits using parametric coupling[J]. Physical Review A, 2023, 108, 062616. doi: 10.1103/PhysRevA.108.062616
61	GONG M , YUAN X , WANG S Y , et al. Experimental exploration of five-qubit quantum error-correcting code with superconducting qubits[J]. National Science Review, 2022, 9 (1): nwab011. doi: 10.1093/nsr/nwab011
62	GUO Q H , ZHAO Y Y , GRASSL M , et al. Testing a quantum error-correcting code on various platforms[J]. Science Bulletin, 2021, 66 (1): 29- 35. doi: 10.1016/j.scib.2020.07.033
63	PICKETT W E . Design for a room-temperature superconductor[J]. Journal of Superconductivity and Novel Magnetism, 2006, 19, 291- 297.
64	KUMAR K , KARN N K , AWANA V P S . Synthesis of possible room temperature superconductor LK-99: Pb₉Cu(PO₄)₆O[J]. Superconductor Science and Technology, 2023, 36, 10.
65	HIRSCH J E . On the ac magnetic susceptibility of a room temperature superconductor: anatomy of a probable scientific fraud[J]. Physica C: Superconductivity and its Applications, 2023, 613, 1354228. doi: 10.1016/j.physc.2023.1354228
66	CAI X , ZHOU B Y , WU Y F , et al. Scaling superconducting quantum chip with highly integratable quantum building blocks[J]. Superconductor Science and Technology, 2023, 36 (8): 085001. doi: 10.1088/1361-6668/acdafe
67	LIANG C, LI Z Y, ZHANG M F, et al. A superconducting quantum chip architecture design method for quantum programs[C]//Proc. of the 3rd International Symposium on Computer Technology and Information Science, 2023: 1115-1121.
68	DUAN P , CHEN Z F , ZHOU Q , et al. Mitigating crosstalk-induced qubit readout error with shallow-neural-network discrimination[J]. Physical Review Applied, 2021, 16 (2): 024063. doi: 10.1103/PhysRevApplied.16.024063
69	HORIKAWA J , KAWAKAMI A , HYODO M , et al. Study of midinfrared superconducting detector with phased-array nanoslot antenna[J]. IEEE Trans.on Applied Superconductivity, 2015, 25 (3): 2301005.
70	DRYAZGOV M A , KORNEEVA Y P , KORNEEV A A . Electrothermal model of a microstrip superconducting detector with photon number resolution[J]. Bulletin of the Russian Academy of Sciences: Physics, 2022, 86 (6): 678- 682. doi: 10.3103/S1062873822060107
71	CHENG X Y , WANG R , YANG N X , et al. Thermal transport in multiple majorana edge states of hybrid topological superconductor junctions[J]. Solid State Communications, 2023, 369, 115182. doi: 10.1016/j.ssc.2023.115182
72	YANG Y S , TANG G X , YAO C , et al. Possible kondo scattering and its signature in seebeck coefficient in topological superconductor Fe1+yTe0.55Se0.45[J]. Journal of Magnetism and Magnetic Materials, 2022, 564, 170126. doi: 10.1016/j.jmmm.2022.170126
73	DENG Y T , HE Y . Exact solutions of topological superconductor model with Hubbard interactions[J]. Physics Letters A, 2021, 397, 127260. doi: 10.1016/j.physleta.2021.127260
74	LARA G A , RAMOS-ANDRADE J P , ZAMBRANO D , et al. Kondo effect in a quantum dot embedded between topological superconductors[J]. Physica E: Low-dimensional Systems and Nanostructures, 2023, 152, 115743. doi: 10.1016/j.physe.2023.115743
75	HAO Z F , ZOU K , MENG Y , et al. High-performance eight-channel system with fractal superconducting nanowire single-photon detectors[J]. Chip, 2024, 3 (2): 100087. doi: 10.1016/j.chip.2024.100087
76	HU X L , CHENG Y H , GU C , et al. Superconducting nanowire single-photon detectors: recent progress[J]. Science Bulletin, 2015, 60 (23): 1980- 1983. doi: 10.1007/s11434-015-0960-3
77	KIM H , BOSE R , SHEN T C , et al. A quantum logic gate between a solid-state quantum bit and a photon[J]. Nature Photonics, 2013, 7 (5): 373- 377. doi: 10.1038/nphoton.2013.48
78	HE L , LIU D N , GAO J X , et al. Super-compact universal quantum logic gates with inverse-designed elements[J]. Science Advances, 2023, 9 (21) doi: 10.1126/sciadv.adg6685
79	LUO W , CAO L , SHI Y Z , et al. Recent progress in quantum photonic chips for quantum communication and internet[J]. Light: Science & Applications, 2023, 12 (1): 175.

阶段	特征
阶段	文献数量a	文献增长率b	专利数量c	专利增长率(平均) d	专利权人数量e	IPC数量f	IPC聚集度g	参考文献数量h
萌芽期	少, a=c	低	少, a=c	低	少	少	低	大
成长期	多, a>c	高	多, a>c	高	多	大	大	大
成熟期	a < c	低, >0	a < c	低, >0	少	d_f>d_g	d_f>d_g	小

一级指标	二级指标	三级指标	含义/数据源/阈值
前沿性	前瞻性	阶段性	处于技术生命周期曲线萌芽期、成长期(文献、专利)
	前瞻性	方向引领性	以ESI高被引论文作者的研究文献表征关键学者的关注方向(文献) 基金资助文献表征关键组织的关注方向(文献)
	先导性	关联性	分类码数量表征学科多样性(文献, 阈值: 大于平均值) IPC数量表征技术领域多样性(专利, 阈值: 大于平均值)
	先导性	影响力	文献被引频次(文献, 阈值: 大于平均值) 权利要求书项数(专利, 阈值: 大于平均值)
	探索性	新颖性	文献平均出版时间(文献, 阈值: 大于平均值) 专利平均公开时间(专利, 阈值: 大于平均值)
		创新性	对某领域文献的所有关键词进行成对组合, 如果这种组合在之前的文献中没有出现, 说明产生了新组合, 该组合被认为是具有创新性(文献, 阈值: 大于0.5)
		创新性	对某领域专利的所有IPC大组进行成对组合, 如果这种组合在之前的技术中没有出现, 说明产生了新组合, 该组合被认为是具有创新性(专利, 阈值: 大于0.5)
	颠覆性	独创性	借助专利引用领域的多样性, 即专利的后向引用是否属于其他技术领域, 对技术的突破性程度进行判断(专利, 阈值: 大于0.5)
	颠覆性	潜在价值	目标专利在不同国家或地区专利申请的数量(专利, 阈值: 大于平均值)

子领域名称	DI专利检索表达式	EI文献检索表达式	是否具备前瞻性
超导量子计算	TAB=((superconducting ADJ quantum) or (superconducting ADJ quantum ADJ comput*) or (superconducting ADJ computer NEAR5 device))	STA=((superconducting quantum) or (superconducting quantum comput*) or (superconducting computer NEAR/5 device))	是
量子加密技术	TAB=((quantum ADJ encrypt) or (quantum near5encrypt) or (quantum ADJ cryptograph*))	STA=((quantum encrypt) or (quantum NEAR/5 encrypt) or (quantum cryptograph*))	否
基于光子的量子计算技术	TAB=((light ADJ quantum ADJ comput) or (optical ADJ quantum ADJ comput) or (single ADJ photon ADJ source) or (ultra-low ADJ loss ADJ light ADJ quantum ADJ line) or (single ADJ photon ADJ detector))	STA=((light quantum comput) or (optical quantum comput) or (single photon source) or (ultra-low loss light quantum line) or (single photon detector))	是
离子阱量子计算机	TAB=((trapped ADJ ion ADJ quantum ADJ comput*) or (quantum ADJ coherence) or (Ion ADJ trap)or(trapped ADJ ion))	STA=((trapped ion quantum comput*) or (quantum coherence) or (Ion trap) or (trapped ion))	否

子领域名称	IPC数量平均值	权利要求书项数平均值	专利平均公开年份	分类码数量平均值	被引频次平均值	出版时间平均年份
超导量子计算	1.97	13.21	1997	5.15	47.84	1991
基于光子的量子计算技术	1.79	10.35	2017	6.05	32.29	1993

子领域名称	技术主题名称
超导量子计算	主题1:低温制冷技术
	主题2:超导电路技术
	主题3:量子纠错技术
	主题4:室温超导技术
	主题5:超导量子芯片技术
	主题6:超导探测技术
	主题7:拓扑超导体
基于光子的量子计算技术	主题1:单光子探测技术
	主题2:光量子逻辑门
	主题3:光量子芯片技术
	主题4:单光子源技术
	主题5:光量子放大技术
	主题6:量子降噪技术
	主题7:量子光子集成电路技术

Frontier technology identification method and empirical research based on data mining

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