Quantum Machine Learning
86 papers from DOAJ
Quantum Machine Learning Research Context
This category covers quantum machine learning research, including quantum kernels, variational classifiers, hybrid learning systems, generative models, and QML benchmarks.
Showing 37-48 of 86
Background Factors Affecting the Radiation Exposure of the Lens of the Eye among Nurses in Interventional Radiology: A Quantitative Observational Study
Tomoko Kuriyama, Takashi Moritake, Koichi Nakagami, Koichi Morota, Go Hitomi, Hiroko Kitamura
Biclustering a dataset using photonic quantum computing
Ajinkya Borle, Ameya Bhave
Chemical Inhibitors in Gas Hydrate Formation: A Review of Modelling Approaches
Njabulo Mziwandile Zulu, Hamed Hashemi, Kaniki Tumba
Designing a Robust Quantum Signature Protocol Based on Quantum Key Distribution for E-Voting Applications
Sunil Prajapat, Urmika Gautam, Deepika Gautam, Pankaj Kumar, Athanasios V. Vasilakos
HUST-Grace2024: a new GRACE-only gravity field time series based on more than 20 years of satellite geodesy data and a hybrid processing chain
H. Zhou, H. Zhou, L. Zheng, L. Zheng, Y. Li, Y. Li, X. Guo, X. Guo, Z. Zhou, Z. Zhou, Z. Luo, Z. Luo
Hybrid data-driven and physics-based modeling for viscosity prediction of ionic liquids
Jing Fan, Zhengxing Dai, Jian Cao, Liwen Mu, Xiaoyan Ji, Xiaohua Lu
Identifying the determining factors of detonation properties for linear nitroaliphatics with high-throughput computation and machine learning
Wen Qian, Jing Huang, Shi-tai Guo, Bo-wen Duan, Wei-yu Xie, Jian Liu, Chao-yang Zhang
Improving student understanding of the number of distinct many-particle states for a system of identical particles with a fixed number of available single-particle states
Christof Keebaugh, Emily Marshman, Chandralekha Singh
Modelling Quantum Software
Luis Mariano Bibbo, Alejandro Fernandez, José Manuel Suarez, Oscar Pastor
Multiscale chemogenetic dissection of fronto-temporal top-down regulation for object memory in primates
Toshiyuki Hirabayashi, Yuji Nagai, Yuki Hori, Yukiko Hori, Kei Oyama, Koki Mimura, Naohisa Miyakawa, Haruhiko Iwaoki, Ken-ichi Inoue, Tetsuya Suhara, Masahiko Takada, Makoto Higuchi, Takafumi Minamimoto
Quantum-accurate machine learning potentials for metal-organic frameworks using temperature driven active learning
Abhishek Sharma, Stefano Sanvito
Rozwój technologii kwantowej: wyzwania i aspekty regulacyjne. Przegląd wybranych zagadnień prawnych
Dorota Glaza-Jankowska