May 2025· Scientific Reports· Vol 16· 2 citations· 63 references
MedicineComputer SciencePhysics
TL;DR
A novel quantum generative model for synthesizing tabular data by proposing a quantum generative adversarial network architecture with flexible data encoding and a novel quantum circuit ansatz for effectively modeling tabular data is introduced.
Abstract
In this paper, we introduce a novel quantum generative model for synthesizing tabular data. Synthetic data is valuable in scenarios where real-world data is scarce or private, as it can be used to augment or replace existing datasets. As enterprise data is predominantly tabular and heterogeneous, often consisting of both categorical and numerical features, this task is relevant across various industries such as healthcare, finance, and software. Existing quantum generative models are designed for homogeneous data; we seek to fill this gap by proposing a quantum generative adversarial network architecture with flexible data encoding and a novel quantum circuit ansatz for effectively modeling tabular data. The proposed approach is tested on the MIMIC-III healthcare and Adult Census datasets, with extensive benchmarking against leading classical models, CTGAN, CopulaGAN, VAE-GMM, and an LLM-based approach using the be-GReaT framework for tabular data synthesis. We evaluated our model as a proof-of-concept on reduced feature subsets using a noiseless statevector simulator on classical hardware. Our simulations show that, for the MIMIC-III dataset, our quantum model achieves competitive, and in some cases, leading performance with respect to an overall similarity score used in the open-source Python library SDMetrics. Additionally, we evaluate the generalization capabilities of the models using two custom-designed metrics that demonstrate the ability of the proposed quantum model to generate useful and novel tabular samples.
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.
Kirill Katsuba, S. Ivanov, S. Polyakov et al.· Educational Data Mining· 0 citations
Findings establish QCBM as a viable complementary tool for data augmentation, particularly for low-dimensional structured tabular data with class imbalance, particularly for low-dimensional structured tabular data with class imbalance.
Tanapol Nuatho, Narisorn Sangnakara, Prapong Prechaprapranwong et al.· 0 citations
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.
Mabrouka Salmi, Dalia Atif, S. Ventura· IEEE Access· 0 citations
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
Hadi Koubeissy, Michel El Khoury, Marc Kamradt et al.· Annual International Compute...· 0 citations
Overall, the results show that quantum and hybrid quantum-classical generative models can learn non-trivial discrete probability distributions but that their effectiveness depends strongly on the selected quantum model, ansatz, and training objective.
This work takes a minimalist approach to designing a competitive generative model that produces high quality samples at fast speed and achieves an FID of 2.56 on ImageNet 256 and simultaneously attains good precision and recall.
A new machine-learning framework aims to improve the success rate of computational protein design while moving away from results that reproduce sequences found in nature.
