Quantumboost: Leveraging Parameterized Quantum Circuits for Imbalanced Dataset Oversampling

Imbalanced datasets in machine learning often result in biased models, particularly in classification problems when certain classes are un‎der-represented. Traditional oversampling techniques like Synthetic Minority Over-sampling Technique (SMOTE), although popular, have ‎difficulty producing synthetic samples of high quality when working in high-dimensional spaces or non-linear feature spaces. Existing techniques, such as SMOTE, do not fully account for the inherent complexity of non-linear structures of a minority class's distribution, which can ‎produce negatively biased classifiers with poor generalization and the risk of overfitting to the minority class. This is further complicated by ‎the reliance of traditional oversampling techniques on linear interpolation, which limits the possibilities of generating realistic or diverse and ‎non-redundant synthetic samples. The proposed technique, QGOPQC, describes Quantum Generative Oversampling using Parameterized ‎Quantum Circuits (PQCs). This approach describes using a quantum circuit, utilizing a novel implementation of a PQCs to overcome the non-‎linear problem. The development of synthetic samples of high fidelity using parameterized quantum circuits would better reflect the true data ‎manifold of the generated class, allowing for a better learning process in a quantum training process, and using quantum state encoding, quantum circuit training, and classical optimization to produce diverse samples of synthetic classes while not compromising on information that ‎maintains feature correlations. Demonstrated through a variety of results, the method achieved consistently higher AUC scores, reaching up to ‎‎0.92 compared to SMOTE’s 0.53, especially in high-dimensional scenarios. The proposed process of using QGOPQC has had a related beneficial effect on classifier performance when trained on imbalanced data, allowing for the process of generating non-redundant high-quality ‎samples through a quantum method of sampling‎.