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Quantum machine learning

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Cited by:

  1. Jing, Hang & Li, Yan & Brandsema, Matthew J. & Chen, Yousu & Yue, Meng, 2024. "HHL algorithm with mapping function and enhanced sampling for model predictive control in microgrids," Applied Energy, Elsevier, vol. 361(C).
  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. 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. Vicente Moret-Bonillo & Samuel Magaz-Romero & Eduardo Mosqueira-Rey, 2022. "Quantum Computing for Dealing with Inaccurate Knowledge Related to the Certainty Factors Model," Mathematics, MDPI, vol. 10(2), pages 1-21, January.
  5. Sheshadri Chatterjee & Ranjan Chaudhuri & Sachin Kamble & Shivam Gupta & Uthayasankar Sivarajah, 2023. "Adoption of Artificial Intelligence and Cutting-Edge Technologies for Production System Sustainability: A Moderator-Mediation Analysis," Information Systems Frontiers, Springer, vol. 25(5), pages 1779-1794, October.
  6. 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.
  7. Li, Nianqiao & Yan, Fei & Hirota, Kaoru, 2022. "Quantum data visualization: A quantum computing framework for enhancing visual analysis of data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 599(C).
  8. Wu, Jiang & Ou, Guiyan & Liu, Xiaohui & Dong, Ke, 2022. "How does academic education background affect top researchers’ performance? Evidence from the field of artificial intelligence," Journal of Informetrics, Elsevier, vol. 16(2).
  9. Stan Lipovetsky, 2023. "Quantum-like Data Modeling in Applied Sciences: Review," Stats, MDPI, vol. 6(1), pages 1-9, February.
  10. Olawale Ayoade & Pablo Rivas & Javier Orduz, 2022. "Artificial Intelligence Computing at the Quantum Level," Data, MDPI, vol. 7(3), pages 1-16, February.
  11. 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.
  12. Liu, Hai-Ling & Yu, Chao-Hua & Wan, Lin-Chun & Qin, Su-Juan & Gao, Fei & Wen, Qiaoyan, 2022. "Quantum mean centering for block-encoding-based quantum algorithm," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 607(C).
  13. Rosch-Grace, Dominic & Straub, Jeremy, 2022. "Analysis of the likelihood of quantum computing proliferation," Technology in Society, Elsevier, vol. 68(C).
  14. Lei, Jiancheng & Song, Tingting & Liu, Ling & Zhang, Kejia, 2023. "An improved quantum algorithm for data fitting," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 613(C).
  15. Ajagekar, Akshay & You, Fengqi, 2019. "Quantum computing for energy systems optimization: Challenges and opportunities," Energy, Elsevier, vol. 179(C), pages 76-89.
  16. 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.
  17. Xiaoxuan Pan & Zhide Lu & Weiting Wang & Ziyue Hua & Yifang Xu & Weikang Li & Weizhou Cai & Xuegang Li & Haiyan Wang & Yi-Pu Song & Chang-Ling Zou & Dong-Ling Deng & Luyan Sun, 2023. "Deep quantum neural networks on a superconducting processor," Nature Communications, Nature, vol. 14(1), pages 1-7, December.
  18. Johannes Herrmann & Sergi Masot Llima & Ants Remm & Petr Zapletal & Nathan A. McMahon & Colin Scarato & François Swiadek & Christian Kraglund Andersen & Christoph Hellings & Sebastian Krinner & Nathan, 2022. "Realizing quantum convolutional neural networks on a superconducting quantum processor to recognize quantum phases," Nature Communications, Nature, vol. 13(1), pages 1-7, December.
  19. Ajagekar, Akshay & You, Fengqi, 2021. "Quantum computing based hybrid deep learning for fault diagnosis in electrical power systems," Applied Energy, Elsevier, vol. 303(C).
  20. Jonas Jäger & Roman V. Krems, 2023. "Universal expressiveness of variational quantum classifiers and quantum kernels for support vector machines," Nature Communications, Nature, vol. 14(1), pages 1-7, December.
  21. Gong, Li-Hua & Xiang, Ling-Zhi & Liu, Si-Hang & Zhou, Nan-Run, 2022. "Born machine model based on matrix product state quantum circuit," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 593(C).
  22. 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.
  23. Ajagekar, Akshay & You, Fengqi, 2022. "Quantum computing and quantum artificial intelligence for renewable and sustainable energy: A emerging prospect towards climate neutrality," Renewable and Sustainable Energy Reviews, Elsevier, vol. 165(C).
  24. Laura Böhm & Sebastian Kolb & Thomas Plankenbühler & Jonas Miederer & Simon Markthaler & Jürgen Karl, 2023. "Short-Term Natural Gas and Carbon Price Forecasting Using Artificial Neural Networks," Energies, MDPI, vol. 16(18), pages 1-25, September.
  25. Jurgita Bruneckiene & Robertas Jucevicius & Ineta Zykiene & Jonas Rapsikevicius & Mantas Lukauskas, 2019. "Assessment of Investment Attractiveness in European Countries by Artificial Neural Networks: What Competences are Needed to Make a Decision on Collective Well-Being?," Sustainability, MDPI, vol. 11(24), pages 1-23, December.
  26. Naughtin, Claire & Hajkowicz, Stefan & Schleiger, Emma & Bratanova, Alexandra & Cameron, Alicia & Zamin, T & Dutta, A, 2022. "Our Future World: Global megatrends impacting the way we live over coming decades," MPRA Paper 113900, University Library of Munich, Germany.
  27. 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.
  28. Guo, Mingchao & Liu, Hailing & Li, Yongmei & Li, Wenmin & Gao, Fei & Qin, Sujuan & Wen, Qiaoyan, 2022. "Quantum algorithms for anomaly detection using amplitude estimation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 604(C).
  29. Daniel J. Egger & Claudio Gambella & Jakub Marecek & Scott McFaddin & Martin Mevissen & Rudy Raymond & Andrea Simonetto & Stefan Woerner & Elena Yndurain, 2020. "Quantum Computing for Finance: State of the Art and Future Prospects," Papers 2006.14510, arXiv.org, revised Jan 2021.
  30. Ojas Parekh, 2023. "Synergies Between Operations Research and Quantum Information Science," INFORMS Journal on Computing, INFORMS, vol. 35(2), pages 266-273, March.
  31. 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.
  32. Zhengmeng Xu & Yujie Wang & Xiaotong Feng & Yilin Wang & Yanli Li & Hai Lin, 2023. "Quantum-Enhanced Forecasting: Leveraging Quantum Gramian Angular Field and CNNs for Stock Return Predictions," Papers 2310.07427, arXiv.org, revised Dec 2023.
  33. Liu, Ling & Song, Tingting & Sun, Zhiwei & Lei, Jiancheng, 2022. "Quantum generative adversarial networks based on Rényi divergences," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 607(C).
  34. Li, Jing & Gao, Fei & Lin, Song & Guo, Mingchao & Li, Yongmei & Liu, Hailing & Qin, Sujuan & Wen, QiaoYan, 2023. "Quantum k-fold cross-validation for nearest neighbor classification algorithm," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 611(C).
  35. Serena Di Giorgio & Paulo Mateus, 2021. "On the Complexity of Finding the Maximum Entropy Compatible Quantum State," Mathematics, MDPI, vol. 9(2), pages 1-24, January.
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