Suchergebnisse - "quantum convolutional autoencoder"

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

Autoren: Nalineekumari Arasavali Mogadala Vinod Kumar Sasibhushana Rao Gottapu et al.

Quelle: Sci Rep
Scientific Reports, Vol 15, Iss 1, Pp 1-17 (2025)

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

Zugangs-URL: https://pubmed.ncbi.nlm.nih.gov/40082537
https://doaj.org/article/baf00827405b4ae7bdeb1dbfabf77448

Quantum Circuits for Quantum Convolutions: A Quantum Convolutional Autoencoder

Autoren: Orduz, Javier Rivas, Pablo Baker, Erich

Schlagwörter: Machine Learning, FOS: Computer and information sciences, Quantum Physics, FOS: Physical sciences, Quantum Physics (quant-ph), I.2.6, F.1.2, Machine Learning (cs.LG), 81P68, 68T07

Zugangs-URL: http://arxiv.org/abs/2509.00637

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

Autoren: Meghana R Sowmyashree Sakrepatna Ramesha Adwitiya Mukhopadhyay

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

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

Dateibeschreibung: electronic resource

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

Zugangs-URL: https://doaj.org/article/5286ad309eb3460d901086c120afb62a

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

Autoren: Tara Kit Kimsay Pov Kimleang Kea et al.

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

Schlagwörter: 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

Verfügbarkeit: 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

Autoren: Astorga Castillo, Dante Friman, Lukas

Schlagwörter: 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

Dateibeschreibung: application/pdf

Zugangs-URL: 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

Autoren: Astorga Castillo, Dante Friman, Lukas

Schlagwörter: 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

Dateibeschreibung: application/pdf

Relation: TRITA-EECS-EX; 2024:389

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

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

Autoren: Kit, Tara Pov, Kimsay Kea, Kimleang et al.

Quelle: Machine Learning: Science & Techonology; 2025, Vol. 6 Issue 4, p1-15, 15p

A QUANTUM CONVOLUTION AUTOENCODER FOR HANDWRITTEN LETTERS AND DIGITS: A CASE STUDY.

Autoren: SAWERWAIN, MAREK KOWAL, MAREK KORBICZ, JÓZEF

Quelle: International Journal of Applied Mathematics & Computer Science; 2025, Vol. 35 Issue 4, p651-666, 16p

Quantum Algorithms for Feature Selection and Compressed Feature Representation of Data

Additional Titles: Kvantalgoritmer för Funktionsval och Datakompression

Autoren: Laius Lundgren, William

Index Begriffe: Feature selection, autoencoders, quantum machine learning, quantum circuits, quantum annealing, Funktionsval, datakompression, kvantmaskininlärning, kvantalgoritmer, kvantkretsar, Physical Sciences, Fysik, Student thesis, info:eu-repo/semantics/bachelorThesis, text

URL: http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-337083
TRITA-SCI-GRU ; 2023:296

Enhancing a Convolutional Autoencoder with a Quantum Approximate Optimization Algorithm for Image Noise Reduction

Autoren: Kit, Tara Pov, Kimsay Kea, Kimleang et al.

Schlagwörter: Quantum Physics

Relation: http://arxiv.org/abs/2401.06367

Verfügbarkeit: http://arxiv.org/abs/2401.06367

Enhancing a Convolutional Autoencoder with a Quantum Approximate Optimization Algorithm for Image Noise Reduction

Autoren: Kea, Kimleang Chang, Won-Du Park, Hee Chul et al.

Schlagwörter: Quantum Physics

Zugangs-URL: http://arxiv.org/abs/2401.06367

Quantum Algorithms for Feature Selection and Compressed Feature Representation of Data ; Kvantalgoritmer för Funktionsval och Datakompression

Autoren: Laius Lundgren, William

Schlagwörter: Feature selection, autoencoders, quantum machine learning, quantum circuits, quantum annealing, Funktionsval, datakompression, kvantmaskininlärning, kvantalgoritmer, kvantkretsar, Physical Sciences, Fysik

Dateibeschreibung: application/pdf

Relation: TRITA-SCI-GRU; 2023:296

Verfügbarkeit: http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-337083