Develops a threshold-regularized Moore-Penrose pseudoinverse formulation of PGM with hybrid classical-quantum circuit implementation using block-encoding for stable discrimination in ill-conditioned and rank-deficient ensembles.
Available: https://journals.plos.org/plosone/article?id= 10.1371/journal.pone.0216224
2 Pith papers cite this work. Polarity classification is still indexing.
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QSMOTE variants with PGM and KPGM classifiers outperform Random Forest on imbalanced Telco churn data, reaching 0.8512 accuracy and 0.8234 F1 using stereo encoding with two quantum copies.
citing papers explorer
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Robust Pretty Good Measurement via Hybrid Classical-Quantum Pseudoinverse Approximation and Circuit-Level Realization
Develops a threshold-regularized Moore-Penrose pseudoinverse formulation of PGM with hybrid classical-quantum circuit implementation using block-encoding for stable discrimination in ill-conditioned and rank-deficient ensembles.
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QSMOTE-PGM/kPGM: QSMOTE Based PGM and kPGM for Imbalanced Dataset Classification
QSMOTE variants with PGM and KPGM classifiers outperform Random Forest on imbalanced Telco churn data, reaching 0.8512 accuracy and 0.8234 F1 using stereo encoding with two quantum copies.