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Noise-induced barren plateaus in variational quantum algorithms

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Cited by:

  1. Liang Xiang & Wenjie Jiang & Zehang Bao & Zixuan Song & Shibo Xu & Ke Wang & Jiachen Chen & Feitong Jin & Xuhao Zhu & Zitian Zhu & Fanhao Shen & Ning Wang & Chuanyu Zhang & Yaozu Wu & Yiren Zou & Jiar, 2024. "Long-lived topological time-crystalline order on a quantum processor," Nature Communications, Nature, vol. 15(1), pages 1-9, December.
  2. 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.
  3. Eric R. Anschuetz & Bobak T. Kiani, 2022. "Quantum variational algorithms are swamped with traps," Nature Communications, Nature, vol. 13(1), pages 1-10, December.
  4. 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.
  5. 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.
  6. 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.
  7. Enrico Fontana & Dylan Herman & Shouvanik Chakrabarti & Niraj Kumar & Romina Yalovetzky & Jamie Heredge & Shree Hari Sureshbabu & Marco Pistoia, 2024. "Characterizing barren plateaus in quantum ansätze with the adjoint representation," Nature Communications, Nature, vol. 15(1), pages 1-12, December.
  8. Dylan Harley & Ishaun Datta & Frederik Ravn Klausen & Andreas Bluhm & Daniel Stilck França & Albert H. Werner & Matthias Christandl, 2024. "Going beyond gadgets: the importance of scalability for analogue quantum simulators," Nature Communications, Nature, vol. 15(1), pages 1-16, December.
  9. 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.
  10. M. Akhtar & F. Bonus & F. R. Lebrun-Gallagher & N. I. Johnson & M. Siegele-Brown & S. Hong & S. J. Hile & S. A. Kulmiya & S. Weidt & W. K. Hensinger, 2023. "A high-fidelity quantum matter-link between ion-trap microchip modules," Nature Communications, Nature, vol. 14(1), pages 1-8, December.
  11. Kurowski, Krzysztof & Pecyna, Tomasz & Slysz, Mateusz & Różycki, Rafał & Waligóra, Grzegorz & Wȩglarz, Jan, 2023. "Application of quantum approximate optimization algorithm to job shop scheduling problem," European Journal of Operational Research, Elsevier, vol. 310(2), pages 518-528.
  12. Huang, Fangyu & Tan, Xiaoqing & Huang, Rui & Xu, Qingshan, 2022. "Variational convolutional neural networks classifiers," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 605(C).
  13. Marco Sciorilli & Lucas Borges & Taylor L. Patti & Diego García-Martín & Giancarlo Camilo & Anima Anandkumar & Leandro Aolita, 2025. "Towards large-scale quantum optimization solvers with few qubits," Nature Communications, Nature, vol. 16(1), pages 1-9, December.
  14. 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.
  15. 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.
  16. He, Zhimin & Deng, Maijie & Zheng, Shenggen & Li, Lvzhou & Situ, Haozhen, 2023. "GSQAS: Graph Self-supervised Quantum Architecture Search," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 630(C).
  17. 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).
  18. 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).
  19. 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.
  20. Antoine Jacquier & Oleksiy Kondratyev & Gordon Lee & Mugad Oumgari, 2023. "Quantum Computing for Financial Mathematics," Papers 2311.06621, arXiv.org.
  21. 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.
  22. Zhao, Xiumei & Li, Yongmei & Li, Jing & Wang, Shasha & Wang, Song & Qin, Sujuan & Gao, Fei, 2024. "Near-term quantum algorithm for solving the MaxCut problem with fewer quantum resources," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 648(C).
  23. Alexander Gresch & Martin Kliesch, 2025. "Guaranteed efficient energy estimation of quantum many-body Hamiltonians using ShadowGrouping," Nature Communications, Nature, vol. 16(1), pages 1-13, December.
  24. Skavysh, Vladimir & Priazhkina, Sofia & Guala, Diego & Bromley, Thomas R., 2023. "Quantum monte carlo for economics: Stress testing and macroeconomic deep learning," Journal of Economic Dynamics and Control, Elsevier, vol. 153(C).
  25. Fangjun Hu & Saeed A. Khan & Nicholas T. Bronn & Gerasimos Angelatos & Graham E. Rowlands & Guilhem J. Ribeill & Hakan E. Türeci, 2024. "Overcoming the coherence time barrier in quantum machine learning on temporal data," Nature Communications, Nature, vol. 15(1), pages 1-12, December.
  26. 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.
  27. El Amine Cherrat & Snehal Raj & Iordanis Kerenidis & Abhishek Shekhar & Ben Wood & Jon Dee & Shouvanik Chakrabarti & Richard Chen & Dylan Herman & Shaohan Hu & Pierre Minssen & Ruslan Shaydulin & Yue , 2023. "Quantum Deep Hedging," Papers 2303.16585, arXiv.org, revised Nov 2023.
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