Quantum Machine Learning
3,957 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 805-816 of 3,957
Prime Factorization Using Partially Constrained Multiple Quantum Annealing With Analytical and Pattern-Based Variable Reduction
Xinyi Guo, Geguang Miao, Shinichi Nishizawa, Shinji Kimura, Takashi Sato
Probabilistic Design of Parametrized Quantum Circuits through Local Gate Modifications
Grier M. Jones, Aviraj Newatia, Alexander Lao, Aditya K. Rao, Viki Kumar Prasad, Hans-Arno Jacobsen
Probabilistic modeling over permutations using quantum computers
Vasilis Belis, Giulio Crognaletti, Matteo Argenton, Michele Grossi, Maria Schuld
Probing quantum entanglement with generalized parton distributions at the electron-ion collider
Anonymous
Program-Level Curriculum Analysis of U.S. Quantum Masters Degrees; Implications for Workforce Preparation
Tunde Kushimo, Bradley Holt, Muhammad Talal
Programmable Memristive Optical Antenna Via Plasmonic Tunneling Junction.
Wu Y, Song D, Lu Z, Zhang H, Guan H, Ke W, Zhang S, Xu H
Proline-Based Structural Rules for Predicting Prolyl Endopeptidase Inhibitory Peptides from Food Proteins: In Vitro Validation and Multiscale Computational Characterization.
Huang SM, Li ML, Liu PJ, Hsu KC
Prostate cancer classification using quantum machine‑learning on multiparametric MRI
Peng Chen, Mojtaba Safari, Rowan Barker-Clarke, Xiaofeng Yang, Jacob G. Scott
Prototype-Based Classifiers and Vector Quantization on a Quantum Computer-Implementing Integer Arithmetic Oracles for Nearest Prototype Search.
Engelsberger A, Pšeničkova M, Villmann T.
Provable and scalable quantum Gaussian processes for quantum learning
Jonas Jäger, Paolo Braccia, Pablo Bermejo, Manuel G. Algaba, Diego García-Martín, M. Cerezo
Provable quantum speedups for computing persistence in topological data analysis
Anonymous
PUBO Formulation for MST and Application to Optimum-Path Forest
Guilherme E. L. Pexe, Lucas A. M. Rattighieri, Leandro A. Passos, Danilo S. Jodas, Douglas Rodrigues, Felipe F. Fanchini, João P. Papa, Kelton A. P. Costa