NEB-adapted ravine ensembles for QNNs classifying concentratable entanglement outperform naive methods when local-prediction variability is high and reduce costs, with ravines persisting under depth and qubit scaling.
and Nakhl, Azar C
2 Pith papers cite this work. Polarity classification is still indexing.
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Schmidt decomposition low-rank approximation yields up to 97% circuit depth reduction in FRQI quantum image encoding with MSE of 0.27 while preserving visual quality.
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Ravines in quantum cost landscapes: opportunities for improved VQA predictions
NEB-adapted ravine ensembles for QNNs classifying concentratable entanglement outperform naive methods when local-prediction variability is high and reduce costs, with ravines persisting under depth and qubit scaling.