Matrix eigenvalue solver based on reconfigurable photonic neural network

The solution of matrix eigenvalues has always been a research hotspot in the field of modern numerical analysis, which has important value in practical application of engineering technology and scientific research. Despite the fact that currently existing algorithms for solving the eigenvalues of ma...
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Autor*in:	Liao Kun [verfasserIn] Li Chentong [verfasserIn] Dai Tianxiang [verfasserIn] Zhong Chuyu [verfasserIn] Lin Hongtao [verfasserIn] Hu Xiaoyong [verfasserIn] Gong Qihuang [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	graphene/si thermo-optical modulation matrix eigenvalue solver reconfigurable photonic neural network saturated absorption effect

Übergeordnetes Werk:	In: Nanophotonics - De Gruyter, 2016, 11(2022), 17, Seite 4089-4099
Übergeordnetes Werk:	volume:11 ; year:2022 ; number:17 ; pages:4089-4099

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1515/nanoph-2022-0109

Katalog-ID:	DOAJ030304008

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allfields	10.1515/nanoph-2022-0109 doi (DE-627)DOAJ030304008 (DE-599)DOAJ29aa8068584a4a3da4324e2cfb4930ff DE-627 ger DE-627 rakwb eng QC1-999 Liao Kun verfasserin aut Matrix eigenvalue solver based on reconfigurable photonic neural network 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The solution of matrix eigenvalues has always been a research hotspot in the field of modern numerical analysis, which has important value in practical application of engineering technology and scientific research. Despite the fact that currently existing algorithms for solving the eigenvalues of matrices are well-developed to try to satisfy both in terms of computational accuracy and efficiency, few of them have been able to be realized on photonic platform. The photonic neural network not only has strong judgment in solving inference tasks due to the superior learning ability, but also makes full use of the advantages of photonic computing with ultrahigh speed and ultralow energy consumption. Here, we propose a strategy of an eigenvalue solver for real-value symmetric matrices based on reconfigurable photonic neural networks. The strategy shows the feasibility of solving the eigenvalues of real-value symmetric matrices of n × n matrices with locally connected networks. Experimentally, we demonstrate the task of solving the eigenvalues of 2 × 2, 3 × 3, and 4 × 4 real-value symmetric matrices based on graphene/Si thermo-optical modulated reconfigurable photonic neural networks with saturated absorption nonlinear activation layer. The theoretically predicted test set accuracy of the 2 × 2 matrices is 93.6% with the measured accuracy of 78.8% in the experiment by the standard defined for simplicity of comparison. This work not only provides a feasible solution for the on-chip integrated photonic realization of eigenvalue solving of real-value symmetric matrices, but also lays the foundation for a new generation of intelligent on-chip integrated all-optical computing. graphene/si thermo-optical modulation matrix eigenvalue solver reconfigurable photonic neural network saturated absorption effect Physics Li Chentong verfasserin aut Dai Tianxiang verfasserin aut Zhong Chuyu verfasserin aut Lin Hongtao verfasserin aut Hu Xiaoyong verfasserin aut Gong Qihuang verfasserin aut In Nanophotonics De Gruyter, 2016 11(2022), 17, Seite 4089-4099 (DE-627)720169909 (DE-600)2674162-3 21928614 nnns volume:11 year:2022 number:17 pages:4089-4099 https://doi.org/10.1515/nanoph-2022-0109 kostenfrei https://doaj.org/article/29aa8068584a4a3da4324e2cfb4930ff kostenfrei https://doi.org/10.1515/nanoph-2022-0109 kostenfrei https://doaj.org/toc/2192-8614 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 11 2022 17 4089-4099
spelling	10.1515/nanoph-2022-0109 doi (DE-627)DOAJ030304008 (DE-599)DOAJ29aa8068584a4a3da4324e2cfb4930ff DE-627 ger DE-627 rakwb eng QC1-999 Liao Kun verfasserin aut Matrix eigenvalue solver based on reconfigurable photonic neural network 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The solution of matrix eigenvalues has always been a research hotspot in the field of modern numerical analysis, which has important value in practical application of engineering technology and scientific research. Despite the fact that currently existing algorithms for solving the eigenvalues of matrices are well-developed to try to satisfy both in terms of computational accuracy and efficiency, few of them have been able to be realized on photonic platform. The photonic neural network not only has strong judgment in solving inference tasks due to the superior learning ability, but also makes full use of the advantages of photonic computing with ultrahigh speed and ultralow energy consumption. Here, we propose a strategy of an eigenvalue solver for real-value symmetric matrices based on reconfigurable photonic neural networks. The strategy shows the feasibility of solving the eigenvalues of real-value symmetric matrices of n × n matrices with locally connected networks. Experimentally, we demonstrate the task of solving the eigenvalues of 2 × 2, 3 × 3, and 4 × 4 real-value symmetric matrices based on graphene/Si thermo-optical modulated reconfigurable photonic neural networks with saturated absorption nonlinear activation layer. The theoretically predicted test set accuracy of the 2 × 2 matrices is 93.6% with the measured accuracy of 78.8% in the experiment by the standard defined for simplicity of comparison. This work not only provides a feasible solution for the on-chip integrated photonic realization of eigenvalue solving of real-value symmetric matrices, but also lays the foundation for a new generation of intelligent on-chip integrated all-optical computing. graphene/si thermo-optical modulation matrix eigenvalue solver reconfigurable photonic neural network saturated absorption effect Physics Li Chentong verfasserin aut Dai Tianxiang verfasserin aut Zhong Chuyu verfasserin aut Lin Hongtao verfasserin aut Hu Xiaoyong verfasserin aut Gong Qihuang verfasserin aut In Nanophotonics De Gruyter, 2016 11(2022), 17, Seite 4089-4099 (DE-627)720169909 (DE-600)2674162-3 21928614 nnns volume:11 year:2022 number:17 pages:4089-4099 https://doi.org/10.1515/nanoph-2022-0109 kostenfrei https://doaj.org/article/29aa8068584a4a3da4324e2cfb4930ff kostenfrei https://doi.org/10.1515/nanoph-2022-0109 kostenfrei https://doaj.org/toc/2192-8614 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 11 2022 17 4089-4099
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allfieldsGer	10.1515/nanoph-2022-0109 doi (DE-627)DOAJ030304008 (DE-599)DOAJ29aa8068584a4a3da4324e2cfb4930ff DE-627 ger DE-627 rakwb eng QC1-999 Liao Kun verfasserin aut Matrix eigenvalue solver based on reconfigurable photonic neural network 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The solution of matrix eigenvalues has always been a research hotspot in the field of modern numerical analysis, which has important value in practical application of engineering technology and scientific research. Despite the fact that currently existing algorithms for solving the eigenvalues of matrices are well-developed to try to satisfy both in terms of computational accuracy and efficiency, few of them have been able to be realized on photonic platform. The photonic neural network not only has strong judgment in solving inference tasks due to the superior learning ability, but also makes full use of the advantages of photonic computing with ultrahigh speed and ultralow energy consumption. Here, we propose a strategy of an eigenvalue solver for real-value symmetric matrices based on reconfigurable photonic neural networks. The strategy shows the feasibility of solving the eigenvalues of real-value symmetric matrices of n × n matrices with locally connected networks. Experimentally, we demonstrate the task of solving the eigenvalues of 2 × 2, 3 × 3, and 4 × 4 real-value symmetric matrices based on graphene/Si thermo-optical modulated reconfigurable photonic neural networks with saturated absorption nonlinear activation layer. The theoretically predicted test set accuracy of the 2 × 2 matrices is 93.6% with the measured accuracy of 78.8% in the experiment by the standard defined for simplicity of comparison. This work not only provides a feasible solution for the on-chip integrated photonic realization of eigenvalue solving of real-value symmetric matrices, but also lays the foundation for a new generation of intelligent on-chip integrated all-optical computing. graphene/si thermo-optical modulation matrix eigenvalue solver reconfigurable photonic neural network saturated absorption effect Physics Li Chentong verfasserin aut Dai Tianxiang verfasserin aut Zhong Chuyu verfasserin aut Lin Hongtao verfasserin aut Hu Xiaoyong verfasserin aut Gong Qihuang verfasserin aut In Nanophotonics De Gruyter, 2016 11(2022), 17, Seite 4089-4099 (DE-627)720169909 (DE-600)2674162-3 21928614 nnns volume:11 year:2022 number:17 pages:4089-4099 https://doi.org/10.1515/nanoph-2022-0109 kostenfrei https://doaj.org/article/29aa8068584a4a3da4324e2cfb4930ff kostenfrei https://doi.org/10.1515/nanoph-2022-0109 kostenfrei https://doaj.org/toc/2192-8614 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 11 2022 17 4089-4099
allfieldsSound	10.1515/nanoph-2022-0109 doi (DE-627)DOAJ030304008 (DE-599)DOAJ29aa8068584a4a3da4324e2cfb4930ff DE-627 ger DE-627 rakwb eng QC1-999 Liao Kun verfasserin aut Matrix eigenvalue solver based on reconfigurable photonic neural network 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The solution of matrix eigenvalues has always been a research hotspot in the field of modern numerical analysis, which has important value in practical application of engineering technology and scientific research. Despite the fact that currently existing algorithms for solving the eigenvalues of matrices are well-developed to try to satisfy both in terms of computational accuracy and efficiency, few of them have been able to be realized on photonic platform. The photonic neural network not only has strong judgment in solving inference tasks due to the superior learning ability, but also makes full use of the advantages of photonic computing with ultrahigh speed and ultralow energy consumption. Here, we propose a strategy of an eigenvalue solver for real-value symmetric matrices based on reconfigurable photonic neural networks. The strategy shows the feasibility of solving the eigenvalues of real-value symmetric matrices of n × n matrices with locally connected networks. Experimentally, we demonstrate the task of solving the eigenvalues of 2 × 2, 3 × 3, and 4 × 4 real-value symmetric matrices based on graphene/Si thermo-optical modulated reconfigurable photonic neural networks with saturated absorption nonlinear activation layer. The theoretically predicted test set accuracy of the 2 × 2 matrices is 93.6% with the measured accuracy of 78.8% in the experiment by the standard defined for simplicity of comparison. This work not only provides a feasible solution for the on-chip integrated photonic realization of eigenvalue solving of real-value symmetric matrices, but also lays the foundation for a new generation of intelligent on-chip integrated all-optical computing. graphene/si thermo-optical modulation matrix eigenvalue solver reconfigurable photonic neural network saturated absorption effect Physics Li Chentong verfasserin aut Dai Tianxiang verfasserin aut Zhong Chuyu verfasserin aut Lin Hongtao verfasserin aut Hu Xiaoyong verfasserin aut Gong Qihuang verfasserin aut In Nanophotonics De Gruyter, 2016 11(2022), 17, Seite 4089-4099 (DE-627)720169909 (DE-600)2674162-3 21928614 nnns volume:11 year:2022 number:17 pages:4089-4099 https://doi.org/10.1515/nanoph-2022-0109 kostenfrei https://doaj.org/article/29aa8068584a4a3da4324e2cfb4930ff kostenfrei https://doi.org/10.1515/nanoph-2022-0109 kostenfrei https://doaj.org/toc/2192-8614 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 11 2022 17 4089-4099
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callnumber-first	Q - Science
author	Liao Kun
spellingShingle	Liao Kun misc QC1-999 misc graphene/si thermo-optical modulation misc matrix eigenvalue solver misc reconfigurable photonic neural network misc saturated absorption effect misc Physics Matrix eigenvalue solver based on reconfigurable photonic neural network
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topic_title	QC1-999 Matrix eigenvalue solver based on reconfigurable photonic neural network graphene/si thermo-optical modulation matrix eigenvalue solver reconfigurable photonic neural network saturated absorption effect
topic	misc QC1-999 misc graphene/si thermo-optical modulation misc matrix eigenvalue solver misc reconfigurable photonic neural network misc saturated absorption effect misc Physics
topic_unstemmed	misc QC1-999 misc graphene/si thermo-optical modulation misc matrix eigenvalue solver misc reconfigurable photonic neural network misc saturated absorption effect misc Physics
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title	Matrix eigenvalue solver based on reconfigurable photonic neural network
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title_full	Matrix eigenvalue solver based on reconfigurable photonic neural network
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author_browse	Liao Kun Li Chentong Dai Tianxiang Zhong Chuyu Lin Hongtao Hu Xiaoyong Gong Qihuang
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title_sort	matrix eigenvalue solver based on reconfigurable photonic neural network
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title_auth	Matrix eigenvalue solver based on reconfigurable photonic neural network
abstract	The solution of matrix eigenvalues has always been a research hotspot in the field of modern numerical analysis, which has important value in practical application of engineering technology and scientific research. Despite the fact that currently existing algorithms for solving the eigenvalues of matrices are well-developed to try to satisfy both in terms of computational accuracy and efficiency, few of them have been able to be realized on photonic platform. The photonic neural network not only has strong judgment in solving inference tasks due to the superior learning ability, but also makes full use of the advantages of photonic computing with ultrahigh speed and ultralow energy consumption. Here, we propose a strategy of an eigenvalue solver for real-value symmetric matrices based on reconfigurable photonic neural networks. The strategy shows the feasibility of solving the eigenvalues of real-value symmetric matrices of n × n matrices with locally connected networks. Experimentally, we demonstrate the task of solving the eigenvalues of 2 × 2, 3 × 3, and 4 × 4 real-value symmetric matrices based on graphene/Si thermo-optical modulated reconfigurable photonic neural networks with saturated absorption nonlinear activation layer. The theoretically predicted test set accuracy of the 2 × 2 matrices is 93.6% with the measured accuracy of 78.8% in the experiment by the standard defined for simplicity of comparison. This work not only provides a feasible solution for the on-chip integrated photonic realization of eigenvalue solving of real-value symmetric matrices, but also lays the foundation for a new generation of intelligent on-chip integrated all-optical computing.
abstractGer	The solution of matrix eigenvalues has always been a research hotspot in the field of modern numerical analysis, which has important value in practical application of engineering technology and scientific research. Despite the fact that currently existing algorithms for solving the eigenvalues of matrices are well-developed to try to satisfy both in terms of computational accuracy and efficiency, few of them have been able to be realized on photonic platform. The photonic neural network not only has strong judgment in solving inference tasks due to the superior learning ability, but also makes full use of the advantages of photonic computing with ultrahigh speed and ultralow energy consumption. Here, we propose a strategy of an eigenvalue solver for real-value symmetric matrices based on reconfigurable photonic neural networks. The strategy shows the feasibility of solving the eigenvalues of real-value symmetric matrices of n × n matrices with locally connected networks. Experimentally, we demonstrate the task of solving the eigenvalues of 2 × 2, 3 × 3, and 4 × 4 real-value symmetric matrices based on graphene/Si thermo-optical modulated reconfigurable photonic neural networks with saturated absorption nonlinear activation layer. The theoretically predicted test set accuracy of the 2 × 2 matrices is 93.6% with the measured accuracy of 78.8% in the experiment by the standard defined for simplicity of comparison. This work not only provides a feasible solution for the on-chip integrated photonic realization of eigenvalue solving of real-value symmetric matrices, but also lays the foundation for a new generation of intelligent on-chip integrated all-optical computing.
abstract_unstemmed	The solution of matrix eigenvalues has always been a research hotspot in the field of modern numerical analysis, which has important value in practical application of engineering technology and scientific research. Despite the fact that currently existing algorithms for solving the eigenvalues of matrices are well-developed to try to satisfy both in terms of computational accuracy and efficiency, few of them have been able to be realized on photonic platform. The photonic neural network not only has strong judgment in solving inference tasks due to the superior learning ability, but also makes full use of the advantages of photonic computing with ultrahigh speed and ultralow energy consumption. Here, we propose a strategy of an eigenvalue solver for real-value symmetric matrices based on reconfigurable photonic neural networks. The strategy shows the feasibility of solving the eigenvalues of real-value symmetric matrices of n × n matrices with locally connected networks. Experimentally, we demonstrate the task of solving the eigenvalues of 2 × 2, 3 × 3, and 4 × 4 real-value symmetric matrices based on graphene/Si thermo-optical modulated reconfigurable photonic neural networks with saturated absorption nonlinear activation layer. The theoretically predicted test set accuracy of the 2 × 2 matrices is 93.6% with the measured accuracy of 78.8% in the experiment by the standard defined for simplicity of comparison. This work not only provides a feasible solution for the on-chip integrated photonic realization of eigenvalue solving of real-value symmetric matrices, but also lays the foundation for a new generation of intelligent on-chip integrated all-optical computing.
collection_details	GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700
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title_short	Matrix eigenvalue solver based on reconfigurable photonic neural network
url	https://doi.org/10.1515/nanoph-2022-0109 https://doaj.org/article/29aa8068584a4a3da4324e2cfb4930ff https://doaj.org/toc/2192-8614
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author2	Li Chentong Dai Tianxiang Zhong Chuyu Lin Hongtao Hu Xiaoyong Gong Qihuang
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Matrix eigenvalue solver based on reconfigurable photonic neural network

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