Quantum Machine Learning
4,009 papers
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 1669-1680 of 4,009
Practical Use Cases of Neutral Atoms Quantum Computers
Matteo Grotti, Sara Marzella, Gabriella Bettonte, Daniele Ottaviani, Elisa Ercolessi
Prediction of Molecular Single-Photon Emitters: A Materials-Modelling Approach
Erik Karlsson Öhman, Daqing Wang, R. Matthias Geilhufe, Christian Schäfer
Prediction of Molecular Structures and Properties by Using Quantum Technology
Ravuri Krishna
Prospects for quantum advantage in machine learning from the representability of functions
Sergi Masot-Llima, Elies Gil-Fuster, Carlos Bravo-Prieto, Jens Eisert, Tommaso Guaita
PROSPECTS FOR THE APPLICATION OF QUANTUM COMPUTING IN ONBOARD COMPUTING SYSTEMS OF ROBOTIC COMPLEXES
N.А. Bocharov, N. B. Paramonov
Protecting Quantum Circuits Through Compiler-Resistant Obfuscation
Pradyun Parayil, Amal Raj, Vivek Balachandran
Pseudorandom Function from Learning Burnside Problem
Dhiraj K. Pandey, Antonio R. Nicolosi
Pulsed learning for quantum data re-uploading models
Ignacio B. Acedo, Pablo Rodriguez-Grasa, Pablo Garcia-Azorin, Javier Gonzalez-Conde
Q-BAR: Blogger Anomaly Recognition via Quantum-enhanced Manifold Learning
Maida Wang, Panyun Jiang
Q-RUN: Quantum-Inspired Data Re-uploading Networks
Wenbo Qiao, Shuaixian Wang, Peng Zhang, Yan Ming, Jiaming Zhao
qc-kmeans: A Quantum Compressive K-Means Algorithm for NISQ Devices
Pedro Chumpitaz-Flores, My Duong, Ying Mao, Kaixun Hua
qcMol: a large-scale dataset of 1.2 million molecules with high-quality quantum chemical annotations for molecular representation learning
Gong H, Wang H, Zhang Z.