[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-82200-en":3,"doc-seo-82200-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":13,"seo_description":14,"update_tm":28,"read_time":29},82200,2336464648746,"Skyler","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Quantum-Enhanced Synthetic Data Generation Using Quantum Circuit Born Machines for Imbalanced Tabular Learning","Data scarcity and class imbalance undermine machine learning generalization while introducing predictive bias. A hybrid quantum-classical framework is presented for synthetic data generation using a Quantum Circuit Born Machine (QCBM) to model complex tabular probability distributions through superposition and entanglement. Experiments on Iris and Telco Customer Churn use normalization and PCA-based dimensionality reduction, basis encoding, and KL-divergence training with gradient parameter-shift optimization. QCBM augmentation improves minority F1 by about 5–15% and minority recall by 10–25%, with 3–10% TSTR/TRTS fidelity gaps and competitive results versus SMOTE variants and MMD reductions.","Quantum-Enhanced Synthetic Data Generation Using Quantum Circuit Born Machines for Imbalanced Tabular Learning  \nTanapol Nuatho  \nDepartment of Computer Engineering King Mongkut’s University of Technology Thonburi Bangkok, Thailand [tanapol.nuat@kmutt.ac.th](tanapol.nuat@kmutt.ac.th)  \nNarisorn Sangnakara  \nDepartment of Computer Engineering King Mongkut’s University of Technology Thonburi Bangkok, Thailand [narisorn.sangn@kmutt.ac.th](narisorn.sangn@kmutt.ac.th)  \nPrapong Prechaprapranwong  \nDepartment of Computer Engineering King Mongkut’s University of Technology Thonburi Bangkok, Thailand [prapong.pre@kmutt.ac.th](prapong.pre@kmutt.ac.th)  \nRajchawit Sarochawikasit  \nDepartment of Computer Engineering King Mongkut’s University of Technology Thonburi Bangkok, Thailand [rajchawit.saro@kmutt.ac.th](rajchawit.saro@kmutt.ac.th)  \narXiv :2607 .09113v1 [ quant-ph] 10 Jul 2026  \nAbstract—Data scarcity and class imbalance are persistent challenges in machine learning that degrade model generalization and introduce predictive bias. This paper presents a hybrid quantum-classical framework for synthetic data generation using a Quantum Circuit Born Machine (QCBM) to address these limitations. The proposed approach exploits quantum mechanical properties—namely superposition and entanglement—within a parameterized variational quantum circuit to model complex probability distributions that are difficult for classical generative methods to capture. Experiments are conducted on two tabular benchmark datasets: the Iris dataset and the Telco Customer Churn dataset. Data preprocessing includes normalization and Principal Component Analysis (PCA)-based dimensionality reduction to enable efficient basis encoding for quantum circuits. The QCBM is trained by minimizing Kullback–Leibler (KL) divergence between the real and generated data distributions using a gradient-based parameter-shift optimization rule. Experimental results demonstrate that augmenting training data with QCBM-generated synthetic samples at 40–50% of the minority class improves F1-score by approximately 5–15% and minorityclass recall by 10–25% . Cross-domain evaluations (Train on Synthetic, Test on Real; and Train on Real, Test on Synthetic) reveal a performance gap of only 3–10%, indicating strong distributional fidelity. Comparative analysis against classical oversampling methods—SMOTE, Borderline-SMOTE, KMeansSMOTE, and SVM-SMOTE—shows that QCBM achieves competitive classification performance and produces lower Maximum Mean Discrepancy (MMD) on the Telco dataset, suggesting superior structural similarity in certain imbalanced settings. These findings establish QCBM as a viable complementary tool for data augmentation, particularly for low-dimensional structured tabular data with class imbalance.  \nIndex Terms—Quantum Machine Learning, Quantum Circuit Born Machine, Synthetic Data Generation, Data Augmentation, Class Imbalance, Hybrid Quantum-Classical, KL Divergence,  \nI. INTRODUCTION  \nModern machine learning pipelines are fundamentally dependent on the availability of large, balanced, and representative training datasets. In practice, however, many realworld domains—including medical diagnostics, fraud detection, and telecommunications churn prediction—suffer from severe class imbalance and limited data collection capacity [1] . Models trained on such imbalanced datasets tend to overfit to the majority class, yielding poor recall on minority classes and limited generalization to unseen samples.  \nSynthetic data generation has emerged as a principled strategy to mitigate these limitations. Classical approaches such as the Synthetic Minority Oversampling Technique (SMOTE)  \n[2] and its variants address class imbalance by interpolating between existing minority-class samples. Generative Adversarial Networks (GANs) [3] and Variational Autoencoders offer more powerful density estimation, but are computationally expensive and prone to training instability, particularly on small ta","cbCaimTHPZTN8SOg","https://ap.wps.com/l/cbCaimTHPZTN8SOg","pdf",244216,2,1,7,"English","en",105,"# Introduction\n## Data scarcity and class imbalance\n## Synthetic data generation methods\n## Quantum computing and QCBM\n## Research gaps and contributions","[{\"question\":\"What problem does the paper address in machine learning datasets?\",\"answer\":\"It addresses data scarcity and class imbalance that degrade generalization and create predictive bias, often causing poor minority-class recall.\"},{\"question\":\"How does the proposed method generate synthetic tabular data?\",\"answer\":\"It trains a Quantum Circuit Born Machine with a parameterized variational quantum circuit, using PCA-based dimensionality reduction and basis encoding, then minimizes KL divergence between real and generated distributions via parameter-shift optimization.\"},{\"question\":\"What evidence shows that QCBM-generated data improves downstream performance?\",\"answer\":\"Experiments on Iris and Telco show that adding QCBM-generated minority-class samples at 40–50% improves F1-score by roughly 5–15% and minority recall by 10–25%, while cross-domain tests show only a 3–10% performance 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problem does the paper address in machine learning datasets?","Question",{"text":75,"@type":76},"It addresses data scarcity and class imbalance that degrade generalization and create predictive bias, often causing poor minority-class recall.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the proposed method generate synthetic tabular data?",{"text":80,"@type":76},"It trains a Quantum Circuit Born Machine with a parameterized variational quantum circuit, using PCA-based dimensionality reduction and basis encoding, then minimizes KL divergence between real and generated distributions via parameter-shift optimization.",{"name":82,"@type":73,"acceptedAnswer":83},"What evidence shows that QCBM-generated data improves downstream performance?",{"text":84,"@type":76},"Experiments on Iris and Telco show that adding QCBM-generated minority-class samples at 40–50% improves F1-score by roughly 5–15% and minority recall by 10–25%, while cross-domain tests show only a 3–10% 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