IDEAS home Printed from https://ideas.repec.org/a/nat/natcom/v15y2024i1d10.1038_s41467-024-47818-z.html
   My bibliography  Save this article

Energy-efficient superparamagnetic Ising machine and its application to traveling salesman problems

Author

Listed:
  • Jia Si

    (National University of Singapore
    Peking University)

  • Shuhan Yang

    (National University of Singapore)

  • Yunuo Cen

    (National University of Singapore)

  • Jiaer Chen

    (National University of Singapore)

  • Yingna Huang

    (National University of Singapore)

  • Zhaoyang Yao

    (National University of Singapore)

  • Dong-Jun Kim

    (National University of Singapore)

  • Kaiming Cai

    (National University of Singapore)

  • Jerald Yoo

    (National University of Singapore)

  • Xuanyao Fong

    (National University of Singapore)

  • Hyunsoo Yang

    (National University of Singapore)

Abstract

The growth of artificial intelligence leads to a computational burden in solving non-deterministic polynomial-time (NP)-hard problems. The Ising computer, which aims to solve NP-hard problems faces challenges such as high power consumption and limited scalability. Here, we experimentally present an Ising annealing computer based on 80 superparamagnetic tunnel junctions (SMTJs) with all-to-all connections, which solves a 70-city traveling salesman problem (TSP, 4761-node Ising problem). By taking advantage of the intrinsic randomness of SMTJs, implementing global annealing scheme, and using efficient algorithm, our SMTJ-based Ising annealer outperforms other Ising schemes in terms of power consumption and energy efficiency. Additionally, our approach provides a promising way to solve complex problems with limited hardware resources. Moreover, we propose a cross-bar array architecture for scalable integration using conventional magnetic random-access memories. Our results demonstrate that the SMTJ-based Ising computer with high energy efficiency, speed, and scalability is a strong candidate for future unconventional computing schemes.

Suggested Citation

  • Jia Si & Shuhan Yang & Yunuo Cen & Jiaer Chen & Yingna Huang & Zhaoyang Yao & Dong-Jun Kim & Kaiming Cai & Jerald Yoo & Xuanyao Fong & Hyunsoo Yang, 2024. "Energy-efficient superparamagnetic Ising machine and its application to traveling salesman problems," Nature Communications, Nature, vol. 15(1), pages 1-12, December.
  • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-47818-z
    DOI: 10.1038/s41467-024-47818-z
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/s41467-024-47818-z
    File Function: Abstract
    Download Restriction: no

    File URL: https://libkey.io/10.1038/s41467-024-47818-z?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. M. W. Johnson & M. H. S. Amin & S. Gildert & T. Lanting & F. Hamze & N. Dickson & R. Harris & A. J. Berkley & J. Johansson & P. Bunyk & E. M. Chapple & C. Enderud & J. P. Hilton & K. Karimi & E. Ladiz, 2011. "Quantum annealing with manufactured spins," Nature, Nature, vol. 473(7346), pages 194-198, May.
    2. William A. Borders & Ahmed Z. Pervaiz & Shunsuke Fukami & Kerem Y. Camsari & Hideo Ohno & Supriyo Datta, 2019. "Integer factorization using stochastic magnetic tunnel junctions," Nature, Nature, vol. 573(7774), pages 390-393, September.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Nihal Sanjay Singh & Keito Kobayashi & Qixuan Cao & Kemal Selcuk & Tianrui Hu & Shaila Niazi & Navid Anjum Aadit & Shun Kanai & Hideo Ohno & Shunsuke Fukami & Kerem Y. Camsari, 2024. "CMOS plus stochastic nanomagnets enabling heterogeneous computers for probabilistic inference and learning," Nature Communications, Nature, vol. 15(1), pages 1-9, December.
    2. John Daniel & Zheng Sun & Xuejian Zhang & Yuanqiu Tan & Neil Dilley & Zhihong Chen & Joerg Appenzeller, 2024. "Experimental demonstration of an on-chip p-bit core based on stochastic magnetic tunnel junctions and 2D MoS2 transistors," Nature Communications, Nature, vol. 15(1), pages 1-11, December.
    3. Andreas Wichert, 2022. "Quantum Tree Search with Qiskit," Mathematics, MDPI, vol. 10(17), pages 1-28, August.
    4. Takuya Funatsu & Shun Kanai & Jun’ichi Ieda & Shunsuke Fukami & Hideo Ohno, 2022. "Local bifurcation with spin-transfer torque in superparamagnetic tunnel junctions," Nature Communications, Nature, vol. 13(1), pages 1-8, December.
    5. Xing Chen & Flavio Abreu Araujo & Mathieu Riou & Jacob Torrejon & Dafiné Ravelosona & Wang Kang & Weisheng Zhao & Julie Grollier & Damien Querlioz, 2022. "Forecasting the outcome of spintronic experiments with Neural Ordinary Differential Equations," Nature Communications, Nature, vol. 13(1), pages 1-12, December.
    6. Jérémie Laydevant & Danijela Marković & Julie Grollier, 2024. "Training an Ising machine with equilibrium propagation," Nature Communications, Nature, vol. 15(1), pages 1-14, December.
    7. Marcello Calvanese Strinati & Claudio Conti, 2022. "Multidimensional hyperspin machine," Nature Communications, Nature, vol. 13(1), pages 1-10, December.
    8. ZhuangEn Fu & Piumi I. Samarawickrama & John Ackerman & Yanglin Zhu & Zhiqiang Mao & Kenji Watanabe & Takashi Taniguchi & Wenyong Wang & Yuri Dahnovsky & Mingzhong Wu & TeYu Chien & Jinke Tang & Allan, 2024. "Tunneling current-controlled spin states in few-layer van der Waals magnets," Nature Communications, Nature, vol. 15(1), pages 1-11, December.
    9. Hakseung Rhee & Gwangmin Kim & Hanchan Song & Woojoon Park & Do Hoon Kim & Jae Hyun In & Younghyun Lee & Kyung Min Kim, 2023. "Probabilistic computing with NbOx metal-insulator transition-based self-oscillatory pbit," Nature Communications, Nature, vol. 14(1), pages 1-8, December.
    10. Kyung Seok Woo & Jaehyun Kim & Janguk Han & Woohyun Kim & Yoon Ho Jang & Cheol Seong Hwang, 2022. "Probabilistic computing using Cu0.1Te0.9/HfO2/Pt diffusive memristors," Nature Communications, Nature, vol. 13(1), pages 1-8, December.
    11. Kang Wang & Yiou Zhang & Vineetha Bheemarasetty & Shiyu Zhou & See-Chen Ying & Gang Xiao, 2022. "Single skyrmion true random number generator using local dynamics and interaction between skyrmions," Nature Communications, Nature, vol. 13(1), pages 1-8, December.
    12. Kevin Wils & Boyang Chen, 2023. "A Symbolic Approach to Discrete Structural Optimization Using Quantum Annealing," Mathematics, MDPI, vol. 11(16), pages 1-29, August.
    13. Bin Yan & Nikolai A. Sinitsyn, 2022. "Analytical solution for nonadiabatic quantum annealing to arbitrary Ising spin Hamiltonian," Nature Communications, Nature, vol. 13(1), pages 1-12, December.
    14. Fabian Böhm & Diego Alonso-Urquijo & Guy Verschaffelt & Guy Van der Sande, 2022. "Noise-injected analog Ising machines enable ultrafast statistical sampling and machine learning," Nature Communications, Nature, vol. 13(1), pages 1-13, December.
    15. M. A. Weiss & A. Herbst & J. Schlegel & T. Dannegger & M. Evers & A. Donges & M. Nakajima & A. Leitenstorfer & S. T. B. Goennenwein & U. Nowak & T. Kurihara, 2023. "Discovery of ultrafast spontaneous spin switching in an antiferromagnet by femtosecond noise correlation spectroscopy," Nature Communications, Nature, vol. 14(1), pages 1-9, December.
    16. Chao Yun & Zhongyu Liang & Aleš Hrabec & Zhentao Liu & Mantao Huang & Leran Wang & Yifei Xiao & Yikun Fang & Wei Li & Wenyun Yang & Yanglong Hou & Jinbo Yang & Laura J. Heyderman & Pietro Gambardella , 2023. "Electrically programmable magnetic coupling in an Ising network exploiting solid-state ionic gating," Nature Communications, Nature, vol. 14(1), pages 1-9, December.
    17. Yuqian Zhao & Zhaohua Ma & Zhangzhen He & Haijun Liao & Yan-Cheng Wang & Junfeng Wang & Yuesheng Li, 2024. "Quantum annealing of a frustrated magnet," Nature Communications, Nature, vol. 15(1), pages 1-9, December.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-47818-z. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.nature.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.