Quantumboost: Leveraging Parameterized Quantum Circuits ‎for Imbalanced Dataset Oversampling

  • Authors

    • D. Gowtham Chakravarthy Assistant professor, Sri Eshwar College of Engineering, Coimbatore- 641202‎
    • S. Kannimuthu Professor, Karpagam College of Engineering, Coimbatore- 641032
    • P. M. Arunkumar Associate Professor, Karpagam College of Engineering, Coimbatore- 641032
    https://doi.org/10.14419/4qgehh16

    Received date: August 26, 2025

    Accepted date: September 30, 2025

    Published date: October 14, 2025

  • Imbalanced Datasets; Machine Learning; Oversampling; Parameterized Quantum Circuits; Quantum Computing; SMOTE‎.

  • Abstract

    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.

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  • How to Cite

    Chakravarthy, D. G. ., Kannimuthu, S. ., & Arunkumar, P. M. . (2025). Quantumboost: Leveraging Parameterized Quantum Circuits ‎for Imbalanced Dataset Oversampling. International Journal of Basic and Applied Sciences, 14(6), 282-288. https://doi.org/10.14419/4qgehh16