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 1129-1140 of 4,009
REGRID-QAOA: A Resource-Efficient Graph-Reduced Hybrid QAOA Framework for Physics-Constrained Power System Islanding
Yuqi Jiang, Yuqi Zhang, Zhiding Liang, Qiang Guan, Yan Li, Ganesh Kumar Venayagamoorthy
Regulatory Frameworks for the Integration of Quantum Computing in Pharmaceutical R&D: Implications for Drug Development and Compliance Monitoring.
Nayan S, Chauhan SB, Singh I, Jain C
Reinforcement Learning for Adaptive Composition of Quantum Circuit Optimisation Passes
Daniel Mills, Ifan Williams, Jacob Swain, Gabriel Matos, Enrico Rinaldi, Alexander Koziell-Pipe
Reinforcement Learning for Enhanced Advanced QEC Architecture Decoding
Yidong Zhou, Lingyi Kong, Yifeng Peng, Zhiding Liang
Reinforcement learning for path integrals in quantum statistical physics
Timour Ichmoukhamedov, Dries Sels
Reinforcement Learning for Quantum Technology
Marin Bukov, Florian Marquardt
Reinforcement Learning for Robust Calibration of Multi-Qudit Quantum Gates
Amine Jaouadi, Sahel Ashhab
Reinforcement learning with learned gadgets to tackle hard quantum problems on real hardware
Akash Kundu, Leopoldo Sarra
Reorganizing Quantum Measurement Records Improves Time-Series Prediction
Markus Baumann, Maximilian Zorn, Thomas Gabor, Claudia Linnhoff-Popien, Jonas Stein
Repair Before Veto, When Repair Is Hidden: Quantum-Accessible Features for Repair-Augmented Constraint Learning
Yifan Wang
Replay-buffer engineering for noise-robust quantum circuit optimization
Akash Kundu, Sebastian Feld
Representation-Induced Symmetry Trapping in Adaptive Variational Quantum Simulations of Multi-Reference Topologies
Hermawan Kresno Dipojono