A2QTGN combines adaptive quantum amplitude encoding with a temporal graph network to improve dynamic link prediction, showing strong results on five benchmark datasets.
Supervised learning with quantum-enhanced feature spaces
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
fields
quant-ph 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
ZZ quantum kernel with binary encoding reaches 66.3% accuracy on 11-feature parity tasks where binary RBF gets 54.3% and other classical methods ~50%, showing a complexity threshold for quantum advantage.
citing papers explorer
-
A2QTGN: Adaptive Amplitude Quantum-Integrated Temporal Graph Network for Dynamic Link Prediction
A2QTGN combines adaptive quantum amplitude encoding with a temporal graph network to improve dynamic link prediction, showing strong results on five benchmark datasets.
-
Quantum Kernels for Parity-Structured Classification: A Hybrid Pipeline
ZZ quantum kernel with binary encoding reaches 66.3% accuracy on 11-feature parity tasks where binary RBF gets 54.3% and other classical methods ~50%, showing a complexity threshold for quantum advantage.