TabularQGAN: a quantum generative model for tabular data synthesis

Synthetic Tabular Data Generation for Data Storage Systems

The generation of synthetic tabular data has emerged as a critical technique for addressing data scarcity, privacy concerns, and the need for augmented training datasets in machine learning applications. This study investigates the applicability and effectiveness of state-of-the-art generative models for synthesizing performance metrics of data storage systems. We utilize a performance dataset encompassing hard disk drive sequential storage configurations characterized by critical indicators including input/output operations per second and latency measurements. Our research methodology follows a systematic approach. First, we establish a baseline using the Synthetic Data Vault library to understand fundamental generative capabilities for tabular data. Subsequently, we implement and evaluate three advanced diffusion-based architectures and one generative adversarial network approach: TabSyn, TabDiff, TabDDPM, and CTGAN. The experimental framework encompasses comprehensive quality assessment through both visual inspection and quantitative metrics. Visual evaluation includes comparative analysis of input/output operations per second and latency distributions, marginal distributions of feature values, and correlation structure preservation between synthetic and real datasets. Quantitative assessment leverages detection scores, shape and trend similarity measures, and machine learning efficacy scores. The experimental results provide insights into the strengths and limitations of each generative approach when applied to storage system performance data exhibiting complex multi-modal distributions and intricate feature correlations.

Quantum-Enhanced Synthetic Data Generation Using Quantum Circuit Born Machines for Imbalanced Tabular Learning

Data scarcity and class imbalance are persistent challenges in machine learning that degrade model generalization and introduce predictive bias. We present 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 -- 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. Preprocessing includes normalization and PCA-based dimensionality reduction to enable efficient basis encoding for quantum circuits. The QCBM is trained by minimizing Kullback-Leibler (KL) divergence between real and generated data distributions using a gradient-based parameter-shift optimization rule. Augmenting training data with QCBM-generated synthetic samples at 40-50% of the minority class improves F1-score by approximately 5-15% and minority-class 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.

HardTVAE: Hardness-Aware Generation and Multi-View Fidelity Evaluation of Synthetic Tabular Data for Imbalanced Learning

We introduce HardTVAE, a hardness-aware tabular variational autoencoder designed to improve synthetic data generation in the presence class imbalance. HardTVAE modulates the evidence lower bound with instance-level hardness weights implemented via static, curriculum, or self-paced strategies, and remains agnostic to the choice among the seventeen supported hardness metrics. Complementing the generative component, we propose a multi-view fidelity framework that integrates distributional, topological, complexity-based, and hardness-based perspectives to capture complementary aspects of data fidelity and reveal structural, geometric, and instance-level properties beyond what any single view captures. The multi-view framework is operationalised by fusing the four fidelity views using the harmonic mean to form the Multi-View Fidelity Index (MFI). We evaluate our approach on ten real-world healthcare datasets, a domain where pronounced class imbalance provides a demanding testbed, and validate all comparisons with non-parametric statistical testing. HardTVAE establishes a new performance trade-off compared to both baselines. It significantly exceeds TVAE in downstream utility while still frequently outranking it in fidelity, and it significantly exceeds CTGAN in multi-view fidelity while remaining ahead on average in utility. This advance is realised most consistently by hardness measures such as Class Likelihood (CL), Collective Feature Efficiency (F4), Maximum Individual Feature Efficiency (F3), Tree Depth Unpruned (TDU), Ratio Intra/Extra Class Distance (N2), whose configurations rank above both baselines on fidelity while securing significant utility gains. The proposed generative model and evaluation framework establish a structured basis for both enhancing and evaluating synthetic data under class imbalance.

Few-Shot LLMs as Synthetic Tabular Data Generators

Synthetic tabular data is increasingly used to support data sharing and machine learning development, where access to real data is restricted or sensitive. However, existing tabular data generators often require dataset-specific training or fine-tuning, data preprocessing, and extensive hyperparameter tuning to achieve plausible results. In addition, existing models may still struggle with mixed data types, modeling complex feature relationships, and pose a privacy risk for information leakage. With the rise of Large Language Models (LLMs) and their expanding capabilities across many domains, we investigate whether few-shot LLMs can be used in a training-free setting as tabular data generators. We propose FewShotTabLLM, a few-shot, training-free generation framework for tabular data that leverages schema-enriched context, representative sampling, and dataset profiling. We evaluate our approach across five widely used tabular benchmark datasets and compare it against strong training-based baselines from different model families (CTGAN, TVAE, TabDDPM, BeGReaT). A three-axis evaluation protocol has been used that covers representativeness, realism, and privacy. Across all evaluations, FewShotTabLLM outperforms baselines and surpasses them on many datasets, achieving realism, boundary adherence, and privacy preservation across all experiments. Overall, the results suggest that training-free fewshot LLM synthesis can serve as an alternative to existing models and a prominent research direction for improving dependency preservation and privacy-aware generation. Our code is available at: https://github.com/BMW-InnovationLab/FewShotTabLLM

Bachelor Data Science and Artificial Intelligence Comparison of Classical, Hybrid Quantum-Classical, and Quantum Generative Models on the Low-Dimensional Bars and Stripes Dataset

ROMS-IMLE: A Minimalist Approach to Competitive Single-Step Generative Modelling

Generative models have undergone many generations of evolution, from VAEs/GANs to diffusion/flow matching. Along the way, the underlying techniques have become more complicated and various beliefs about what drives strong empirical performance have taken hold. Due to the success of diffusion models and flow matching, one of the more common beliefs is the importance of transforming the noise distribution to the data distribution gradually through many small transformations. We ask whether this is truly necessary, and take a minimalist approach to designing a competitive generative model. We start with the bare-bones essentials, namely just a training objective and a model. We purposefully make both simple. For the training objective, we choose Implicit Maximum Likelihood Estimation (IMLE), and eschew more complicated alternatives such as variational inference, adversarial training and numerical integration. For the model, we eschew transformers and instead choose a moderately sized convolutional network. Then we judiciously added elements that are truly essential, which surprisingly do not include iterative denoising. The result is a single-step parameter-efficient generative model that produces high quality samples at fast speed: it achieves an FID of 2.56 on ImageNet 256 and simultaneously attains good precision and recall.