IDEAS home Printed from https://ideas.repec.org/a/eee/chsofr/v196y2025ics0960077925004023.html
   My bibliography  Save this article

Degree of entanglement in Entangled Hidden Markov Models

Author

Listed:
  • Accardi, Luigi
  • Souissi, Abdessatar
  • Soueidi, El Gheteb
  • Rhaima, Mohamed

Abstract

This paper investigates Entangled Hidden Markov Models (EHMMs), with a particular focus on how entanglement influences quantum dynamics. We present a structure theorem for inhomogeneous EHMMs, which provides a foundational understanding of their behavior in complex systems. Furthermore, we compute the Ohya degree of entanglement for models with deterministic stochastic matrices, offering a precise and rigorous way to quantify entanglement in these systems. By applying diagonal restrictions to the observation and hidden algebras, we also demonstrate how classical hidden Markov models (HMMs) naturally arise as a special case of EHMMs. This connection sheds light on the interplay between classical and quantum Markovian processes, bridging the gap between these two frameworks and deepening our understanding of their shared and distinct properties.

Suggested Citation

  • Accardi, Luigi & Souissi, Abdessatar & Soueidi, El Gheteb & Rhaima, Mohamed, 2025. "Degree of entanglement in Entangled Hidden Markov Models," Chaos, Solitons & Fractals, Elsevier, vol. 196(C).
  • Handle: RePEc:eee:chsofr:v:196:y:2025:i:c:s0960077925004023
    DOI: 10.1016/j.chaos.2025.116389
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960077925004023
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.chaos.2025.116389?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Jacob Biamonte & Peter Wittek & Nicola Pancotti & Patrick Rebentrost & Nathan Wiebe & Seth Lloyd, 2017. "Quantum machine learning," Nature, Nature, vol. 549(7671), pages 195-202, September.
    2. Sofiene Jerbi & Casper Gyurik & Simon C. Marshall & Riccardo Molteni & Vedran Dunjko, 2024. "Shadows of quantum machine learning," Nature Communications, Nature, vol. 15(1), pages 1-7, December.
    3. Samson Wang & Enrico Fontana & M. Cerezo & Kunal Sharma & Akira Sone & Lukasz Cincio & Patrick J. Coles, 2021. "Noise-induced barren plateaus in variational quantum algorithms," Nature Communications, Nature, vol. 12(1), pages 1-11, December.
    4. Souissi, Abdessatar & Mukhamedov, Farrukh & Soueidi, El Gheteb & Rhaima, Mohamed & Mukhamedova, Farzona, 2024. "Entangled hidden elephant random walk model," Chaos, Solitons & Fractals, Elsevier, vol. 186(C).
    5. Mohamed, A.-B.A. & Aldosari, F.M. & Younis, S.M. & Eleuch, H., 2023. "Quantum memory and entanglement dynamics induced by interactions of two moving atoms with a coherent cavity," Chaos, Solitons & Fractals, Elsevier, vol. 177(C).
    6. Gyongyosi, Laszlo & Imre, Sandor, 2018. "Multiple access multicarrier continuous-variable quantum key distribution," Chaos, Solitons & Fractals, Elsevier, vol. 114(C), pages 491-505.
    7. Souissi, Abdessatar & Soueidi, El Gheteb, 2023. "Entangled Hidden Markov Models," Chaos, Solitons & Fractals, Elsevier, vol. 174(C).
    8. Albers, Tony & Cisternas, Jaime & Radons, Günter, 2022. "Chaotic diffusion of dissipative solitons: From anti-persistent random walks to Hidden Markov Models," Chaos, Solitons & Fractals, Elsevier, vol. 161(C).
    9. Sofiene Jerbi & Lukas J. Fiderer & Hendrik Poulsen Nautrup & Jonas M. Kübler & Hans J. Briegel & Vedran Dunjko, 2023. "Quantum machine learning beyond kernel methods," Nature Communications, Nature, vol. 14(1), pages 1-8, December.
    10. Hsin-Yuan Huang & Michael Broughton & Masoud Mohseni & Ryan Babbush & Sergio Boixo & Hartmut Neven & Jarrod R. McClean, 2021. "Power of data in quantum machine learning," Nature Communications, Nature, vol. 12(1), pages 1-9, December.
    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. Souissi, Abdessatar & Mukhamedov, Farrukh & Soueidi, El Gheteb & Rhaima, Mohamed & Mukhamedova, Farzona, 2024. "Entangled hidden elephant random walk model," Chaos, Solitons & Fractals, Elsevier, vol. 186(C).
    2. Junyu Liu & Minzhao Liu & Jin-Peng Liu & Ziyu Ye & Yunfei Wang & Yuri Alexeev & Jens Eisert & Liang Jiang, 2024. "Towards provably efficient quantum algorithms for large-scale machine-learning models," Nature Communications, Nature, vol. 15(1), pages 1-6, December.
    3. Matthias C. Caro & Hsin-Yuan Huang & M. Cerezo & Kunal Sharma & Andrew Sornborger & Lukasz Cincio & Patrick J. Coles, 2022. "Generalization in quantum machine learning from few training data," Nature Communications, Nature, vol. 13(1), pages 1-11, December.
    4. Sofiene Jerbi & Lukas J. Fiderer & Hendrik Poulsen Nautrup & Jonas M. Kübler & Hans J. Briegel & Vedran Dunjko, 2023. "Quantum machine learning beyond kernel methods," Nature Communications, Nature, vol. 14(1), pages 1-8, December.
    5. Matthias C. Caro & Hsin-Yuan Huang & Nicholas Ezzell & Joe Gibbs & Andrew T. Sornborger & Lukasz Cincio & Patrick J. Coles & Zoë Holmes, 2023. "Out-of-distribution generalization for learning quantum dynamics," Nature Communications, Nature, vol. 14(1), pages 1-9, December.
    6. Xinbiao Wang & Yuxuan Du & Zhuozhuo Tu & Yong Luo & Xiao Yuan & Dacheng Tao, 2024. "Transition role of entangled data in quantum machine learning," Nature Communications, Nature, vol. 15(1), pages 1-8, December.
    7. Elies Gil-Fuster & Jens Eisert & Carlos Bravo-Prieto, 2024. "Understanding quantum machine learning also requires rethinking generalization," Nature Communications, Nature, vol. 15(1), pages 1-12, December.
    8. Isaiah Hull & Or Sattath & Eleni Diamanti & Göran Wendin, 2024. "Quantum Algorithms," Contributions to Economics, in: Quantum Technology for Economists, chapter 0, pages 37-103, Springer.
    9. Eric R. Anschuetz & Bobak T. Kiani, 2022. "Quantum variational algorithms are swamped with traps," Nature Communications, Nature, vol. 13(1), pages 1-10, December.
    10. Alen Senanian & Sridhar Prabhu & Vladimir Kremenetski & Saswata Roy & Yingkang Cao & Jeremy Kline & Tatsuhiro Onodera & Logan G. Wright & Xiaodi Wu & Valla Fatemi & Peter L. McMahon, 2024. "Microwave signal processing using an analog quantum reservoir computer," Nature Communications, Nature, vol. 15(1), pages 1-9, December.
    11. Francesco Hoch & Eugenio Caruccio & Giovanni Rodari & Tommaso Francalanci & Alessia Suprano & Taira Giordani & Gonzalo Carvacho & Nicolò Spagnolo & Seid Koudia & Massimiliano Proietti & Carlo Liorni &, 2025. "Quantum machine learning with Adaptive Boson Sampling via post-selection," Nature Communications, Nature, vol. 16(1), pages 1-11, December.
    12. Wang, Shaoxuan & Shen, Yingtong & Liu, Xinjian & Zhang, Haoying & Wang, Yukun, 2024. "Variational quantum entanglement classification discrimination," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 637(C).
    13. Manuel S. Rudolph & Jacob Miller & Danial Motlagh & Jing Chen & Atithi Acharya & Alejandro Perdomo-Ortiz, 2023. "Synergistic pretraining of parametrized quantum circuits via tensor networks," Nature Communications, Nature, vol. 14(1), pages 1-10, December.
    14. Bingzhi Zhang & Junyu Liu & Xiao-Chuan Wu & Liang Jiang & Quntao Zhuang, 2024. "Dynamical transition in controllable quantum neural networks with large depth," Nature Communications, Nature, vol. 15(1), pages 1-12, December.
    15. Wei-Ming Li & Shi-Ju Ran, 2022. "Non-Parametric Semi-Supervised Learning in Many-Body Hilbert Space with Rescaled Logarithmic Fidelity," Mathematics, MDPI, vol. 10(6), pages 1-15, March.
    16. Supanut Thanasilp & Samson Wang & M. Cerezo & Zoë Holmes, 2024. "Exponential concentration in quantum kernel methods," Nature Communications, Nature, vol. 15(1), pages 1-13, December.
    17. Michael Ragone & Bojko N. Bakalov & Frédéric Sauvage & Alexander F. Kemper & Carlos Ortiz Marrero & Martín Larocca & M. Cerezo, 2024. "A Lie algebraic theory of barren plateaus for deep parameterized quantum circuits," Nature Communications, Nature, vol. 15(1), pages 1-10, December.
    18. Sofiene Jerbi & Casper Gyurik & Simon C. Marshall & Riccardo Molteni & Vedran Dunjko, 2024. "Shadows of quantum machine learning," Nature Communications, Nature, vol. 15(1), pages 1-7, December.
    19. Huang, Chenyi & Zhang, Shibin & Chang, Yan & Yan, Lily, 2024. "Quantum metric learning with fuzzy-informed learning," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 643(C).
    20. Zhang, Zuo-Yuan & Fang, Yu-Yan & Li, Jin-Fang & Hu, Jie-Ru & Liu, Jin-Ming & Sun, Zhaoxi & Huang, Xinning, 2024. "Entropic uncertainty relation and entanglement of molecular dipoles in an electric field," Chaos, Solitons & Fractals, Elsevier, vol. 186(C).

    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:eee:chsofr:v:196:y:2025:i:c:s0960077925004023. 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: Thayer, Thomas R. (email available below). General contact details of provider: https://www.journals.elsevier.com/chaos-solitons-and-fractals .

    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.