Dual-Parameterized Quantum Circuit GAN Model in High Energy Physics
EPJ Web Conf., 251 (2021) 03050
Published online: 2021-08-23
Articles citing this article
The Citing articles tool gives a list of articles citing the current article.
The citing articles come from EDP Sciences database, as well as other publishers participating in CrossRef Cited-by Linking Program. You can set up your personal account to receive an email alert each time this article is cited by a new article (see the menu on the right-hand side of the abstract page).
Cited article:
This article has been cited by the following article(s):
Quantum frontiers in high energy physics
Yaquan Fang, Christina Gao, Ying-Ying Li, Jing Shu, Yusheng Wu, Hongxi Xing, Bin Xu, Lailin Xu and Chen ZhouScience China Physics, Mechanics & Astronomy 68 (6) (2025)
https://doi.org/10.1007/s11433-024-2635-4
HQ-DCGAN: Hybrid quantum deep convolutional generative adversarial network approach for ECG generation
Zhiguo Qu, Weilong Chen and Prayag TiwariKnowledge-Based Systems 301 112260 (2024)
https://doi.org/10.1016/j.knosys.2024.112260
Guided quantum compression for high dimensional data classification
Vasilis Belis, Patrick Odagiu, Michele Grossi, Florentin Reiter, Günther Dissertori and Sofia VallecorsaMachine Learning: Science and Technology 5 (3) 035010 (2024)
https://doi.org/10.1088/2632-2153/ad5fdd
Machine learning for anomaly detection in particle physics
Vasilis Belis, Patrick Odagiu and Thea Klaeboe AarrestadReviews in Physics 12 100091 (2024)
https://doi.org/10.1016/j.revip.2024.100091
Protocols for trainable and differentiable quantum generative modeling
Oleksandr Kyriienko, Annie E. Paine and Vincent E. ElfvingPhysical Review Research 6 (3) (2024)
https://doi.org/10.1103/PhysRevResearch.6.033291
Quantum Quantile Mechanics: Solving Stochastic Differential Equations for Generating Time‐Series
Annie E. Paine, Vincent E. Elfving and Oleksandr KyriienkoAdvanced Quantum Technologies 6 (10) (2023)
https://doi.org/10.1002/qute.202300065
Fitting a collider in a quantum computer: tackling the challenges of quantum machine learning for big datasets
Miguel Caçador Peixoto, Nuno Filipe Castro, Miguel Crispim Romão, Maria Gabriela Jordão Oliveira and Inês OchoaFrontiers in Artificial Intelligence 6 (2023)
https://doi.org/10.3389/frai.2023.1268852
Running the Dual-PQC GAN on noisy simulators and real quantum hardware
Su Yeon Chang, Edwin Agnew, Elías Combarro, et al.Journal of Physics: Conference Series 2438 (1) 012062 (2023)
https://doi.org/10.1088/1742-6596/2438/1/012062
Quantum computing for data-centric engineering and science
Steven HerbertData-Centric Engineering 3 (2022)
https://doi.org/10.1017/dce.2022.36
Multiqubit state learning with entangling quantum generative adversarial networks
S. E. Rasmussen and N. T. ZinnerPhysical Review A 106 (3) (2022)
https://doi.org/10.1103/PhysRevA.106.032429
Conditional Born machine for Monte Carlo event generation
Oriel Kiss, Michele Grossi, Enrique Kajomovitz and Sofia VallecorsaPhysical Review A 106 (2) (2022)
https://doi.org/10.1103/PhysRevA.106.022612
Quantum Computing Applications in Future Colliders
Heather M. Gray and Koji TerashiFrontiers in Physics 10 (2022)
https://doi.org/10.3389/fphy.2022.864823
Quantum integration of elementary particle processes
Gabriele Agliardi, Michele Grossi, Mathieu Pellen and Enrico PratiPhysics Letters B 832 137228 (2022)
https://doi.org/10.1016/j.physletb.2022.137228
Style-based quantum generative adversarial networks for Monte Carlo events
Carlos Bravo-Prieto, Julien Baglio, Marco Cè, et al.Quantum 6 777 (2022)
https://doi.org/10.22331/q-2022-08-17-777
Unsupervised quantum circuit learning in high energy physics
Andrea Delgado and Kathleen E. HamiltonPhysical Review D 106 (9) (2022)
https://doi.org/10.1103/PhysRevD.106.096006
129 (2022)
https://doi.org/10.1109/ICSA-C54293.2022.00033
Hybrid quantum classical graph neural networks for particle track reconstruction
Cenk Tüysüz, Carla Rieger, Kristiane Novotny, et al.Quantum Machine Intelligence 3 (2) (2021)
https://doi.org/10.1007/s42484-021-00055-9