SUANPAN: scalable photonic linear vector machine

Ziyue Yang; Chen Li; Yuqia Ran; Yongzhuo Li; Xue Feng; Kaiyu Cui; Fang Liu; Hao Sun; Wei Zhang; Yu Ye; Fei Qiao; Jiaxing Wang; Cun-Zheng Ning; Connie J. Chang-Hasnain; Yidong Huang

doi:10.1038/s41377-025-02059-7

Your Location：

Home >

Browse articles >

SUANPAN: scalable photonic linear vector machine

Original Articles

- SUANPAN: scalable photonic linear vector machine
- Light: Science & Applications Vol. 15, Issue 1, Pages: 140-150(2026)
- Affiliations：
  
  1.Department of Electronic Engineering, Tsinghua University, 100084 Beijing, China
  2.State Key Laboratory for Mesoscopic Physics and Frontiers Science Center for Nano-Optoelectronics, School of Physics, Peking University, 100871 Beijing, China
  3.Berxel Photonics Company Ltd, 518071 Shenzhen, China
  4.College of Integrated Circuits and Optoelectronic Chips, Shenzhen Technology University, 518118 Shenzhen, China
- Author bio：
  
  Yongzhuo Li (liyongzhuo@tsinghua.edu.cn)
  Xue Feng (x-feng@tsinghua.edu.cn)
  Yidong Huang (yidonghuang@tsinghua.edu.cn)
- Funds：
- DOI：10.1038/s41377-025-02059-7
  CLC：
- Received：09 April 2025，
  
  Revised：2025-09-11，
  
  Accepted：16 September 2025，
  
  Published Online：01 January 2026，
  
  Published：31 January 2026
- Accepted：
Scan QR Code
Yang, Z. Y. et al. SUANPAN: scalable photonic linear vector machine. Light: Science & Applications, 15, 140-150 (2026).
DOI：

Yang, Z. Y. et al. SUANPAN: scalable photonic linear vector machine. Light: Science & Applications, 15, 140-150 (2026). DOI： 10.1038/s41377-025-02059-7.

摘要

Abstract

Photonics is promising to handle extensive vector multiplications in artificial intelligence (AI) techniques due to natural bosonic parallelism and high-speed information transmission. However

the dimensionality of current photonic linear operation is limited and tough to improve due to the complex beam interaction for implementing optica

l matrix operation and digital-analog conversions. Here

we propose a programmable and reconfigurable photonic linear vector machine with extreme scalability formed by a series of emitter-detector pairs as the independent basic computing units. The elemental values of two high-dimensional vectors are prepared on emitter-detector pairs by bit encoding and analog detecting method without requiring large-scale analog-to-digital converter or digital-to-analog converter arrays. Since there is no interaction among light beams inside

extreme scalability could be achieved by simply multiplicating the independent emitter-detector pair. The proposed architecture is inspired by the traditional Chinese Suanpan or abacus

and thus is denoted as photonic SUANPAN. Experimentally

the computing fidelities for vector inner products could achieve

98% in our implementation with an 8 × 8 vertical cavity surface emission laser (VCSEL) array and an 8 × 8 MoTe

two-dimensional material photodetector array. Furthermore

such implementation is applied on two typical AI tasks as 1024-dimensional optimization problem is successfully solved and competitive classification accuracy of 88% is achieved for handwritten digit dataset. We believe that the photonic SUANPAN could serve as a fundamental linear vector machine and enhance various future AI applications.

关键词

Keywords

references

Cetinic, E. & She, J. Understanding and creating art with AI: review and outlook. ACM Trans. Multimed. Comput. Commun. Appl. 18 , 66 (2022)..

Rajpurkar, P. et al. AI in health and medicine. Nat. Med. 28 , 31–38 (2022)..

LeCun, Y., Bengio, Y. & Hinton, G. Deep learning. Nature 521 , 436–444 (2015)..

Wetzstein, G. et al. Inference in artificial intelligence with deep optics and photonics. Nature 588 , 39–47 (2020)..

Fu, T. Z. et al. Optical neural networks: progress and challenges. Light Sci. Appl. 13 , 263 (2024)..

Mohseni, N., McMahon, P. L. & Byrnes, T. Ising machines as hardware solvers of combinatorial optimization problems. Nat. Rev. Phys. 4 , 363–379 (2022)..

Laydevant, J., Marković, D. & Grollier, J. Training an Ising machine with equilibrium propagation. Nat. Commun. 15 , 3671 (2024)..

Nikhar, S. et al. All-to-all reconfigurability with sparse and higher-order Ising machines. Nat. Commun. 15 , 8977 (2024)..

Zhou, H. L. et al. Photonic matrix multiplication lights up photonic accelerator and beyond. Light Sci. Appl. 11 , 30 (2022)..

Goodman, J. W., Dias, A. R. & Woody, L. M. Fully parallel, high-speed incoherent optical method for performing discrete Fourier transforms. Opt. Lett. 2 , 1–3 (1978)..

Spall, J. et al. Fully reconfigurable coherent optical vector-matrix multiplication. Opt. Lett. 45 , 5752–5755 (2020)..

Wang, T. Y. et al. An optical neural network using less than 1 photon per multiplication. Nat. Commun. 13 , 123 (2022)..

Reck, M. et al. Experimental realization of any discrete unitary operator. Phys. Rev. Lett. 73 , 58–61 (1994)..

Shen, Y. C. et al. Deep learning with coherent nanophotonic circuits. Nat. Photonics 11 , 441–446 (2017)..

Roques-Carmes, C. et al. Heuristic recurrent algorithms for photonic Ising machines. Nat. Commun. 11 , 249 (2020)..

Pai, S. et al. Experimentally realized in situ backpropagation for deep learning in photonic neural networks. Science 380 , 398–404 (2023)..

Lin, X. et al. All-optical machine learning using diffractive deep neural networks. Science 361 , 1004–1008 (2018)..

Yan, T. et al. Fourier-space diffractive deep neural network. Phys. Rev. Lett. 123 , 023901 (2019)..

Zhou, T. K. et al. Large-scale neuromorphic optoelectronic computing with a reconfigurable diffractive processing unit. Nat. Photonics 15 , 367–373 (2021)..

Fu, T. Z. et al. Photonic machine learning with on-chip diffractive optics. Nat. Commun. 14 , 70 (2023)..

Tait, A. N. et al. Broadcast and weight: an integrated network for scalable photonic spike processing. J. Light. Technol. 32 , 4029–4041 (2014)..

Deng, Y. B. & Chu, D. P. Coherence properties of different light sources and their effect on the image sharpness and speckle of holographic displays. Sci. Rep. 7 , 5893 (2017)..

Feldmann, J. et al. All-optical spiking neurosynaptic networks with self-learning capabilities. Nature 569 , 208–214 (2019)..

Feldmann, J. et al. Parallel convolutional processing using an integrated photonic tensor core. Nature 589 , 52–58 (2021)..

Bai, B. W. et al. Microcomb-based integrated photonic processing unit. Nat. Commun. 14 , 66 (2023)..

Dong, B. W. et al. Partial coherence enhances parallelized photonic computing. Nature 632 , 55–62 (2024)..

Huo, N. J. & Konstantatos, G. Recent progress and future prospects of 2D-based photodetectors. Adv. Mater. 30 , 1801164 (2018)..

Kim, S. et al. Neuro-CIM:ADC-less neuromorphic computing-in-memory processor with operation gating/stopping and digital–analog networks. IEEE J. Solid-State Circuits 58 , 2931–2945 (2023)..

Chen, Z. J. et al. Deep learning with coherent VCSEL neural networks. Nat. Photonics 17 , 723–730 (2023)..

An, J. R. et al. Perspectives of 2D materials for optoelectronic integration. Adv. Funct. Mater. 32 , 2110119 (2021)..

You, J. et al. Hybrid/integrated silicon photonics based on 2D materials in optical communication nanosystems. Laser Photonics Rev. 14 , 2000239 (2020)..

Koepfli, S. M. et al. Metamaterial graphene photodetector with bandwidth exceeding 500 gigahertz. Science 380 , 1169–1174 (2023)..

Lopez-Sanchez, O. et al. Ultrasensitive photodetectors based on monolayer MoS 2 . Nat. Nanotechnol. 8 , 497–501 (2013)..

Qi, P. F. et al. Remote lightening and ultrafast transition: intrinsic modulation of exciton spatiotemporal dynamics in monolayer MoS 2 . ACS Nano 14 , 6897–6905 (2020)..

Baugher, B. W. H. et al. Optoelectronic devices based on electrically tunable p–n diodes in a monolayer dichalcogenide. Nat. Nanotechnol. 9 , 262–267 (2014)..

Qi , P. F. et al. Phonon scattering and exciton localization: molding exciton flux in two dimensional disorder energy landscape. eLight 1 , 6 (2021)..

Qi, P. F. et al. Giant excitonic upconverted emission from two-dimensional semiconductor in doubly resonant plasmonic nanocavity. Light Sci. Appl. 11 , 176 (2022)..

Lucas, A. Ising formulations of many NP problems. Front. Phys. 2 , 5 (2014)..

van Laarhoven, P. J. M. & Aarts, E. H. L. Simulated Annealing: Theory and Applications (Springer, 1987).

Ouyang, J. Y. et al. On-demand photonic Ising machine with simplified Hamiltonian calculation by phase encoding and intensity detection. Commun. Phys. 7 , 168 (2024)..

Yamamoto, Y. et al. Coherent Ising machines—optical neural networks operating at the quantum limit. npj Quantum Inf. 3 , 49 (2017)..

Haribara, Y., Utsunomiya, S. & Yamamoto, Y. Computational principle and performance evaluation of coherent ising machine based on degenerate optical parametric oscillator network. Entropy 18 , 151 (2016)..

Goemans, M. X. & Williamson, D. P. Improved approximation algorithms for maximum cut and satisfiability problems using semidefinite programming. J. ACM 42 , 1115–1145 (1995)..

Wu, B. et al. A monolithically integrated optical Ising machine. Nat. Commun. 16 , 4296 (2025)..

Ketkar, N. Deep Learning with Python: A Hands-on Introduction (Berkeley: Apress Berkeley, 2017).

Jeong, K. Y. et al. Recent progress in nanolaser technology. Adv. Mater. 32 , 2001996 (2020)..

Du, W. et al. Nanolasers based on 2D materials. Laser Photonics Rev. 14 , 2000271 (2020)..

Zhang, Q. et al. Halide perovskite semiconductor lasers: materials, cavity design, and low threshold. Nano Lett. 21 , 1903–1914 (2021)..

Long, M. S. et al. Progress, challenges, and opportunities for 2D material based photodetectors. Adv. Electron. Mater. 29 , 1803807 (2019)..

Liu, C. Y. et al. Silicon/2D-material photodetectors: from near-infrared to mid-infrared. Light Sci. Appl. 10 , 123 (2021)..

Flöry, N. et al. Waveguide-integrated van der Waals heterostructure photodetector at telecom wavelengths with high speed and high responsivity. Nat. Nanotechnol. 15 , 118–124 (2020)..

Li, R. Z. et al. On-chip metasurface-mediated MoTe 2 photodetector with electrically tunable polarization-sensitivity. Adv. Opt. Mater. 13 , 2402668 (2025)..

Xu, X. L. et al. Millimeter-scale single-crystalline semiconducting MoTe 2 via solid-to-solid phase transformation. J. Am. Chem. Soc. 141 , 2128–2134 (2019)..

Pan, Y. et al. Heteroepitaxy of semiconducting 2H-MoTe 2 thin films on arbitrary surfaces for large-scale heterogeneous integration. Nat. Synth. 1 , 701–708 (2022)..

Views

Downloads

CSCD

Alert me when the article has been cited

Submit

Tools

Publicity Resources

No data

Related Author

No data

Related Institution

No data

⁰