Quantum Machine Learning
486 papers for year 2025
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 133-144 of 486
Evaluating Supervised Learning Approaches for Quantification of Quantum Entanglement
Shruti Aggarwal, Trasha Gupta, R. K. Agrawal, S. Indu
Excitation energies and UV-Vis absorption spectra from INDO/s+ML
Ezekiel Oyeniyi, Omololu Akin-Ojo
Experimental Demonstration of Software-Orchestrated Quantum Network Applications over a Campus-Scale Testbed
Md. Shariful Islam, Joaquin Chung, Ely Marcus Eastman, Robert J. Hayek, Prem Kumar, Rajkumar Kettimuthu
Expert perspectives on the future of quantum physics education at the secondary level
Philipp Bitzenbauer, Malte S. Ubben, Daria Anttila, Maria Bondani, Maria Luisa Chiofalo, Sergej Faletic, Simon Goorney, Franziska Greinert, Rainer Müller, Zdeňka Koupilová, Massimiliano Malgieri, Avraham Merzel, Henk J. Pol, Gesche Pospiech, Heike Kirsten Elisabeth Stadermann, Efraim Yehuda Weissman, Kim Krijtenburg-Lewerissa
Exploiting biased noise in variational quantum models
Connor van Rossum, Sally Shrapnel, Riddhi Gupta
Exploiting Reset Operations in Cloud-based Quantum Computers to Run Quantum Circuits for Free
Jakub Szefer
Exploring international research trends in quantum physics education through a detailed bibliometric analysis
Sangwoo Ha, Chueng-Ryong Ji
EXPLORING MEDIATING ROLES BETWEEN CSR AND CUSTOMER SATISFACTION IN HOTELS
LI NIU, ZULHAMRI ABDULLAH, MASTURA MAHAMED
Exploring the Techniques and Challenges of Privacy-Preserving Data Sharing in Quantum-Enabled Networks
Zaid Rasool, Nisreen Hadi
F2: Offline Reinforcement Learning for Hamiltonian Simulation via Free-Fermionic Subroutine Compilation
Ethan Decker, Christopher Watson, Junyu Zhou, Yuhao Liu, Chenxu Liu, Ang Li, Gushu Li, Samuel Stein
Fair Benchmarking of Optimisation Applications
Frank Phillipson
Fast and Accurate Pixel Calibration of Tof Neutron Diffractometers with Machine Learning
Albert P. Song, Ke An