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.
Quantum classification
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abstract
Quantum classification is defined as the task of predicting the associated class of an unknown quantum state drawn from an ensemble of pure states given a finite number of copies of this state. By recasting the state discrimination problem within the framework of Machine Learning (ML), we can use the notion of learning reduction coming from classical ML to solve different variants of the classification task, such as the weighted binary and the multiclass versions.
fields
cs.LG 1years
2025 1verdicts
UNVERDICTED 1representative citing papers
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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.