Quantum Machine Learning
416 papers for year 2023
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 397-408 of 416
TTN‐FCN: A Tangut character classification framework by tree tensor network and fully connected neural network
Ziping Ma, Jinlin Ma
Two new monoterpenoid indole alkaloids from the kernels of Kopsia arborea.
Chen C, Ding X, Han L, Zhu W, Huang K, Hao X, Zhang Y
Unclonable Non-Interactive Zero-Knowledge
Ruta Jawale, Dakshita Khurana
Unconditionally secure quantum commitments with preprocessing
Luowen Qian
Unifying (Quantum) Statistical and Parametrized (Quantum) Algorithms
Alexander Nietner
Unveiling Advanced Computational Applications in Quantum Computing: A Comprehensive Review
J H Markna, T P Palatia, Smeetraj Gohel, Bharat Kataria
Variational data encoding and correlations in quantum-enhanced machine learning
Ming-Hao Wang, Hua Lu
Variational measurement-based quantum computation for generative modeling
Arunava Majumder, Marius Krumm, Tina Radkohl, Lukas J. Fiderer, Hendrik Poulsen Nautrup, Sofiene Jerbi, Hans J. Briegel
Variational quantum algorithm-preserving feasible space for solving the uncapacitated facility location problem
Sha-Sha Wang, Hai-Ling Liu, Yong-Mei Li, Fei Gao, Su-Juan Qin, Qiao-Yan Wen
Variational quantum and quantum-inspired clustering.
Bermejo P, Orús R.
Variational Quantum Approximated Spectral Clustering
Hyeong-Gyu Kim, Siheon Park, June-Koo Kevin Rhee
Variational Quantum Circuit Design for Quantum Reinforcement Learning on Continuous Environments
Georg Kruse, Theodora-Augustina Dragan, Robert Wille, Jeanette Miriam Lorenz