Quantum Circuits Cut Data Imbalance Recall Gap by 10–25%

A new hybrid quantum-classical framework is demonstrating a measurable improvement in machine learning performance on imbalanced datasets, with researchers reporting a 5, 15% increase in F1-score and minority-class recall improving by 10-25%. The team, comprised of Tanapol Nuatho, Narisorn Sangnakara, Prapong Prechaprapranwong, and Rajchawit Sarochawikasit, developed a Quantum Circuit Born Machine (QCBM) to generate synthetic data that addresses limitations caused by data scarcity and class imbalance. Experiments utilizing the Iris dataset and the Telco Customer Churn dataset show that augmenting training data with QCBM-generated samples at 40-50% of the minority class improves F1-score by approximately 5-15%. Cross-domain evaluations revealed a performance gap of only 3-10%, indicating a high degree of distributional fidelity for the quantum-generated samples. A five to fifteen percent increase in F1-score and minority-class recall improving…