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.
Quantum machine learning in feature Hilbert 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
Magnitude-only encoding reaches 99.57% accuracy on 3-class and 71.19% on 8-class SAR tasks in hybrid models, beating phase-inclusive alternatives, while phase boosts pure quantum models by up to 21.65 points.
citing papers explorer
-
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.
-
Magnitude Is All You Need? Rethinking Phase in Quantum Encoding of Complex SAR Data
Magnitude-only encoding reaches 99.57% accuracy on 3-class and 71.19% on 8-class SAR tasks in hybrid models, beating phase-inclusive alternatives, while phase boosts pure quantum models by up to 21.65 points.