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Approximately Equivariant Quantum Neural Network for $p4m$ Group Symmetries in Images / Chang, Su Yeon (CERN ; Ecole Polytechnique, Lausanne) ; Grossi, Michele (CERN) ; Saux, Bertrand Le (European Space Agency) ; Vallecorsa, Sofia (CERN)
Quantum Neural Networks (QNNs) are suggested as one of the quantum algorithms which can be efficiently simulated with a low depth on near-term quantum hardware in the presence of noises. However, their performance highly relies on choosing the most suitable architecture of Variational Quantum Algorithms (VQAs), and the problem-agnostic models often suffer issues regarding trainability and generalization power. [...]
arXiv:2310.02323.- 2023-09-17 - 7 p. - Published in : 10.1109/QCE57702.2023.00033 Fulltext: 2310.02323 - PDF; Publication - PDF;
In : 2023 International Conference on Quantum Computing and Engineering (QCE23), Bellevue, United States, 17 - 22 Sep 2023, pp.229-235

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(56)

Quantum Convolutional Circuits for Earth Observation Image Classification / Chang, Su Yeon (CERN ; Ecole Polytechnique, Lausanne) ; Le Saux, Bertrand (European Space Agency) ; Vallecorsa, Sofia (CERN) ; Grossi, Michele (CERN)
The amount of study on Quantum Machine Learning (QML) is increasing extensively due to its potential advantages in terms of representational power and computational resources. These advances suggest a possibility to extend its usage into the context of Earth Observations, where Machine Learning (ML) plays an important role due to its extensive amount of data to be manipulated. [...]
2022 - 4 p. - Published in : 10.1109/IGARSS46834.2022.9883992
In : IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2022), Kuala Lumpur, Malaysia, 17 - 22 Jul 2022, pp.4907-4910

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(30)

Benchmarking Quantum Convolutional Neural Networks for Classification and Data Compression Tasks / Khoo, Jun Yong (A-STAR, Singapore) ; Gan, Chee Kwan (A-STAR, Singapore) ; Ding, Wenjun (A-STAR, Singapore) ; Carrazza, Stefano (CERN ; Milan U. ; INFN, Milan) ; Ye, Jun (A-STAR, Singapore ; Technol. Innovation Inst., UAE) ; Kong, Jian Feng (A-STAR, Singapore)
Quantum Convolutional Neural Networks (QCNNs) have emerged as promising models for quantum machine learning tasks, including classification and data compression. [...]
arXiv:2411.13468. - 3.
Fulltext

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(29)

Classical Splitting of Parametrized Quantum Circuits / Tüysüz, Cenk (speaker) (CQTA, DESY and Humboldt University of Berlin)
In this talk we will dive into the topic of barren plateaus and investigate a new method to avoid them. Barren plateaus appear to be a major obstacle for using variational quantum algorithms to simulate large-scale quantum systems or to replace traditional machine learning algorithms [...]
2023 - 2809. QTI Lectures External link: Event details In : Classical Splitting of Parametrized Quantum Circuits

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(27)

Lorentz Group Equivariant Autoencoders / Hao, Zichun (UC, San Diego) ; Kansal, Raghav (UC, San Diego ; Fermilab) ; Duarte, Javier (UC, San Diego) ; Chernyavskaya, Nadezda (CERN)
There has been significant work recently in developing machine learning (ML) models in high energy physics (HEP) for tasks such as classification, simulation, and anomaly detection. Often these models are adapted from those designed for datasets in computer vision or natural language processing, which lack inductive biases suited to HEP data, such as equivariance to its inherent symmetries. [...]
arXiv:2212.07347; FERMILAB-PUB-22-963-V.- 2023-06-09 - 14 p. - Published in : Eur. Phys. J. C 83 (2023) 485 Fulltext: 2212.07347 - PDF; FERMILAB-PUB-22-963-V - PDF; Fulltext from Publisher: PDF; External link: Fermilab Library Server

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(26)

Convolutional Neural Network for Image Recognition / Seifnashri, Sahand (CERN)
The aim of this project is to use machine learning techniques especially Convolutional Neural Networks for image processing. [...]
CERN-STUDENTS-Note-2015-029. - 2015

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(24)

Latent Style-based Quantum GAN for high-quality Image Generation / Chang, Su Yeon (CERN ; Ecole Polytechnique, Lausanne) ; Thanasilp, Supanut (Ecole Polytechnique, Lausanne ; Chulalongkorn U. ; European Space Agency) ; Le Saux, Bertrand (European Space Agency) ; Vallecorsa, Sofia (CERN) ; Grossi, Michele (CERN)
Quantum generative modeling is among the promising candidates for achieving a practical advantage in data analysis. [...]
arXiv:2406.02668. - 28 p.
Fulltext

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(22)

Equivariant neural networks for robust <math display="inline"><mi>C</mi><mi>P</mi></math> observables / Cruz, Sergio Sánchez (CERN) ; Kolosova, Marina (U. Florida, Gainesville (main)) ; Ramón Álvarez, Clara (ICTEA, Oviedo) ; Petrucciani, Giovanni (CERN) ; Vischia, Pietro (ICTEA, Oviedo)
We introduce the usage of equivariant neural networks in the search for violations of the charge-parity (

CP

$\textit{CP}$ ) symmetry in particle interactions at the CERN Large Hadron Collider. We design neural networks that take as inputs kinematic information of recorded events and that transform equivariantly under the a symmetry group related to the

CP

$\textit{CP}$ transformation. [...]
arXiv:2405.13524.- 2024-11-01 - 10 p. - Published in : Phys. Rev. D Fulltext: Publication - PDF; 2405.13524 - PDF;

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(21)

Iris Data Classification Using Quantum Neural Networks / Sahni, Vishal ; Patvardhan, C
2006 - Published in : AIP Conf. Proc.: 864 (2006) , pp. 219-227 External link: Published version from AIP
In : Conference on Quantum Computing: Back Action 2006, Kanpur, India, 6 - 12 Mar 2006, pp.219-227

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10.

(21)

Symmetries in nonperturbative 2-d quantum gravity / La, H S
UPR-0432-T. - 1990. - 10 p.
CERN library copies

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