Výsledky vyhľadávania - "Quantum convolutional autoencoder"

QCAE-QOC-SVM: A hybrid quantum machine learning model for DoS and Fuzzy attack detection on autonomous vehicle CAN bus

Autori: Meghana R Sowmyashree Sakrepatna Ramesha Adwitiya Mukhopadhyay

Zdroj: MethodsX, Vol 15, Iss , Pp 103471- (2025)

Predmety: A hybrid of Quantum Convolutional Autoencoder and Quantum Orthogonal Classifier with SVM, Science

Popis súboru: electronic resource

Relation: http://www.sciencedirect.com/science/article/pii/S2215016125003164; https://doaj.org/toc/2215-0161

Prístupová URL adresa: https://doaj.org/article/5286ad309eb3460d901086c120afb62a

Optimal satellite selection using quantum convolutional autoencoder for low-cost GNSS receiver applications

Autori: Nalineekumari Arasavali Mogadala Vinod Kumar Sasibhushana Rao Gottapu a ďalší

Zdroj: Scientific Reports, Vol 15, Iss 1, Pp 1-17 (2025)

Predmety: Satellite selection, Quantum convolutional autoencoder, GNSS, Combined constellation, Sustainable Development Goal (SDG) 9:Industry, Innovation, and Infrastructure, SDG 11: Sustainable Cities and Communities, Medicine, Science

Popis súboru: electronic resource

Relation: https://doaj.org/toc/2045-2322

Prístupová URL adresa: https://doaj.org/article/baf00827405b4ae7bdeb1dbfabf77448

Enhancing a convolutional autoencoder with a quantum approximate optimization algorithm for image noise reduction

Autori: Tara Kit Kimsay Pov Kimleang Kea a ďalší

Zdroj: Machine Learning: Science and Technology, Vol 6, Iss 4, p 045027 (2025)

Predmety: quantum computing, quantum convolutional autoencoder, variational quantum algorithm, quantum machine learning, Computer engineering. Computer hardware, TK7885-7895, Electronic computers. Computer science, QA75.5-76.95

Relation: https://doi.org/10.1088/2632-2153/ae1656; https://doaj.org/toc/2632-2153; https://doaj.org/article/4efa9a37709b46e288463e3f8e36321f

Dostupnosť: https://doi.org/10.1088/2632-2153/ae1656
https://doaj.org/article/4efa9a37709b46e288463e3f8e36321f

Comparative Analysis of Quantum and Classical Convolutional Autoencoders on the MNIST Dataset

Autori: Astorga Castillo, Dante Friman, Lukas

Predmety: Computer and Information Sciences, Quantum neural network, Quanvolution, Data- och informationsvetenskap, Quantum computing, Kvantkonvolutionerande autokodare, Quantum convolutional autoencoder, Kvantmaskininlärning, Kvantberäkning, Quanvolutional autoencoder, Kvantneurala nätverk, Convolutional autoencoder, Konvolutionerande autokodare, Quantum machine learning

Popis súboru: application/pdf

Prístupová URL adresa: http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-351190

Comparative Analysis of Quantum and Classical Convolutional Autoencoders on the MNIST Dataset ; Komparativ analys av kvantiska och klassiska konvolutionerande autokodare på MNIST datasetet

Autori: Astorga Castillo, Dante Friman, Lukas

Predmety: Quantum computing, Quantum neural network, Quantum machine learning, Convolutional autoencoder, Quantum convolutional autoencoder, Quanvolution, Quanvolutional autoencoder, Kvantberäkning, Kvantneurala nätverk, Kvantmaskininlärning, Konvolutionerande autokodare, Kvantkonvolutionerande autokodare, Computer and Information Sciences, Data- och informationsvetenskap

Popis súboru: application/pdf

Relation: TRITA-EECS-EX; 2024:389

Dostupnosť: http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-351190