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 1213-1224 of 4,009
Sota Voce: Low-Noise Sampling of Sparse Fixed-Weight Vectors
Décio Luiz Gazzoni Filho, Gora Adj, Slim Bettaieb, Alessandro Budroni, Jorge Chávez-Saab, Francisco Rodríguez-Henríquez
Space-sharing and Singleton Bounds for Entanglement-assisted Classical Coding
Yuhang Yao, Tushita Prasad, Markus Grassl, Syed Jafar, Hua Sun
Sparse identification of quantum Hamiltonian dynamics via quantum circuit learning
Yusei Tateyama, Yuzuru Kato
Sparsified Kolmogorov-Arnold Networks for Interpretable Quantum State Tomography
Xinge Wu, Huaxin Wang, Jiajun Liu, Ruiqing He, Jiandong Shang, Hengliang Guo, Qiang Chen
SPATE: Spiking-Phase Adaptive Temporal Encoding for Quantum Machine Learning
Nouhaila Innan, Rachmad Vidya Wicaksana Putra, Muhammad Shafique
SpecML: web tool for predicting the spectral properties of BODIPYs.
Ksenofontov AA, Eremeeva YV, Bocharov PS, Makarov DM
Spectral Filtering for Learning Quantum Dynamics
Elad Hazan, Annie Marsden
Spectral Form Factor of Gapped Random Matrix Systems
Krishan Saraswat
Spectral methods: crucial for machine learning, natural for quantum computers?
Vasilis Belis, Joseph Bowles, Rishabh Gupta, Evan Peters, Maria Schuld
Spectral Phase Encoding for Quantum Kernel Methods
Pablo Herrero Gómez, Antonio Jimeno Morenilla, David Muñoz-Hernández, Higinio Mora Mora
Spectral Quantum Chemistry and Infrared Resonance Library for Data-Driven Molecular Spectroscopy.
Krishnadas A, Kansal J, Charron NE, Ragyanszki A
SpinTune: Improving the Reliability of Quantum Sensor Networks for Practical Quantum-Classical Utility
Jason Ludmir, Nicholas S. DiBrita, Jason Han, Tirthak Patel