mlsanityData to 7 Oct 2026

TabularQGAN: A quantum generative model for tabular data synthesis

arXiv 2505.22533 ↗First posted 28 May 2025cs.LG cs.AI quant-phcited by 2

On the map: Synthetic data and self-improvement for LLMs → Privacy-preserving tabular data synthesis

Abstract (arXiv)

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.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Placed in Privacy-preserving tabular data synthesis, one of 13 directions in Synthetic data and self-improvement for LLMs. Builds on 27 of its 63 references that are in the map. First posted on arXiv 28 May 2025.

Builds on

27 of 63 references.

Closest ancestors · 1

Same direction, newest first

  1. 2023Mixed-Type Tabular Data Synthesis with Score-based Diffusion in Latent Space14 Oct

Other references · 26

  1. 2020Language Models are Few-Shot Learnerscited by 32,199
  2. 2022Generative Adversarial Networkscited by 5,603
  3. 2016XGBoost: A Scalable Tree Boosting Systemcited by 5,120
  4. 2016Membership Inference Attacks against Machine Learning Modelscited by 2,221
  5. 2019Modeling Tabular data using Conditional GANcited by 695
  6. 2020Variational Quantum Algorithmscited by 687
  7. 2018Barren plateaus in quantum neural network training landscapescited by 624
  8. 2020The power of quantum neural networkscited by 377
  9. 2022TabDDPM: Modelling Tabular Data with Diffusion Modelscited by 373
  10. 2022Language Models are Realistic Tabular Data Generatorscited by 290
  11. 2017Generating Multi-label Discrete Patient Records using Generative Adversarial Networkscited by 197
  12. 2018Quantum generative adversarial networkscited by 128
14 more
  1. 2024Better than classical? The subtle art of benchmarking quantum machine learning modelscited by 82
  2. 2023Does provable absence of barren plateaus imply classical simulability?cited by 78
  3. 2020On the Quantum versus Classical Learnability of Discrete Distributionscited by 57
  4. 2023Quantum machine learning for image classificationcited by 56
  5. 2022Theoretical Guarantees for Permutation-Equivariant Quantum Neural Networkscited by 52
  6. 2022Quantum Vision Transformerscited by 48
  7. 2021Generating Synthetic Mixed-type Longitudinal Electronic Health Records for Artificial Intelligent Applicationscited by 47
  8. 2017Information Perspective to Probabilistic Modeling: Boltzmann Machines versus Born Machinescited by 33
  9. 2024Quantum Convolutional Neural Networks are Effectively Classically Simulablecited by 33
  10. 2024A comprehensive review of Quantum Machine Learning: from NISQ to Fault Tolerancecited by 32
  11. 2024Parameter Efficient Quasi-Orthogonal Fine-Tuning via Givens Rotationcited by 21
  12. 2021Quantum kernels for real-world predictions based on electronic health recordscited by 15
  13. 2024An improved tabular data generator with VAE-GMM integrationcited by 13
  14. 2019Quantum Observables for continuous control of the Quantum Approximate Optimization Algorithm via Reinforcement Learningcited by 3

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 2.

2 papers.

  1. 2026Quantum Circuit-Based Learning Models: Bridging Quantum Computing and Machine Learningcited by 1
  2. 2026QTabGAN: A Hybrid Quantum-Classical GAN for Tabular Data Synthesiscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2024MALLM-GAN: Multi-Agent Large Language Model as Generative Adversarial Network for Synthesizing Tabular Data
  2. 2025MediQ-GAN: Quantum-Inspired GAN for High Resolution Medical Image Generation
  3. 2025A Comprehensive Survey of Synthetic Tabular Data Generation
  4. 2020Tabular GANs for uneven distribution
  5. 2024Multi-objective evolutionary GAN for tabular data synthesis
  6. 2025Creating Artificial Students that Never Existed: Leveraging Large Language Models and CTGANs for Synthetic Data Generation
  7. 2023Generating tabular datasets under differential privacy
  8. 2023Generative quantum machine learning via denoising diffusion probabilistic models