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

• URL: http://arxiv.org/abs/2607.09113v1
• Date: Fri, 10 Jul 2026 05:53:29 GMT
• Title: Quantum-Enhanced Synthetic Data Generation Using Quantum Circuit Born Machines for Imbalanced Tabular Learning
• Abstract summary: We present a quantum-classical framework for synthetic data generation using a Quantum Circuit Born Machine (QCBM)<n>The proposed approach exploits quantum mechanical properties -- superposition and entanglement -- within a parameterized variational quantum circuit.<n>We show that QCBM achieves competitive classification performance and produces lower Mean Maximum Discrepancy (MMD) on the Telco dataset.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• Discretization-Aware Fine-Tuning for Quantum Machine Learning with Chemical Foundation Models [0.0]
  Key challenge in practical quantum machine learning (QML) is the limited capacity of near-term quantum devices to encode high-dimensional classical data into small quantum registers.<n>We introduce discretization-aware fine-tuning (DAFT), a method that adapts a pre-trained chemical foundation model to produce representations that remain informative after quantization.<n>We evaluate both quantum and classical models under a controlled setting in which they receive identical discretized bit-string inputs.
  arXiv  Detail & Related papers  (2026-09-02T23:36:29Z)
• Quantum Incremental Learning with Mixed State Prototypes [53.319426778720434]
  We introduce a novel quantum incremental learning framework based on trainable mixed-state prototypes.<n>Our model achieves high-dimensional feature concentration using a minimal number of qubits.
  arXiv  Detail & Related papers  (2026-08-11T04:22:42Z)
• Domain-Aware Hybrid Quantum Learning via Correlation-Guided Circuit Design for Crime Pattern Analytics [54.3410887955176]
  Crime pattern analysis is critical for law enforcement and predictive policing.<n>The surge in criminal activities from rapid urbanization creates high-dimensional, imbalanced datasets.<n>This study presents a quantum-classical comparison framework for crime analytics.
  arXiv  Detail & Related papers  (2026-04-08T06:05:25Z)
• Continual Quantum Architecture Search with Tensor-Train Encoding: Theory and Applications to Signal Processing [68.35481158940401]
  CL-QAS is a continual quantum architecture search framework.<n>It mitigates challenges of costly encoding amplitude and forgetting in variational quantum circuits.<n>It achieves controllable robustness expressivity, sample-efficient generalization, and smooth convergence without barren plateaus.
  arXiv  Detail & Related papers  (2026-01-10T02:36:03Z)
• Winning the Pruning Gamble: A Unified Approach to Joint Sample and Token Pruning for Efficient Supervised Fine-Tuning [71.30276778807068]
  We propose a unified framework that strategically coordinates sample pruning and token pruning.<n>Q-Tuning achieves a +38% average improvement over the full-data SFT baseline using only 12.5% of the original training data.
  arXiv  Detail & Related papers  (2025-09-28T13:27:38Z)
• Enhancing Machine Learning for Imbalanced Medical Data: A Quantum-Inspired Approach to Synthetic Oversampling (QI-SMOTE) [0.0]
  Class imbalance remains a critical challenge in machine learning (ML), particularly in the medical domain.<n>This study introduces Quantum-Inspired SMOTE (QI-SMOTE), a novel data augmentation technique.<n>QI-SMOTE generates synthetic instances that preserve complex data structures, improving model generalization and classification accuracy.
  arXiv  Detail & Related papers  (2025-09-02T22:20:46Z)
• Quantum-Accelerated Neural Imputation with Large Language Models (LLMs) [0.0]
  This paper introduces Quantum-UnIMP, a novel framework that integrates shallow quantum circuits into an LLM-based imputation architecture.<n>Our experiments on benchmark mixed-type datasets demonstrate that Quantum-UnIMP reduces imputation error by up to 15.2% for numerical features (RMSE) and improves classification accuracy by 8.7% for categorical features (F1-Score) compared to state-of-the-art classical and LLM-based methods.
  arXiv  Detail & Related papers  (2025-07-11T02:00:06Z)
• Hybrid Quantum Neural Networks with Amplitude Encoding: Advancing Recovery Rate Predictions [6.699192644249841]
  Recovery rate prediction plays a pivotal role in bond investment strategies by enhancing risk assessment, optimizing portfolio allocation, and improving pricing accuracy.<n>We propose a hybrid Quantum Machine Learning (QML) model with Amplitude.<n>We evaluate the model on a global recovery rate dataset comprising 1,725 observations from 1996 to 2023.
  arXiv  Detail & Related papers  (2025-01-27T07:27:23Z)
• Quantum Kernel Learning for Small Dataset Modeling in Semiconductor Fabrication: Application to Ohmic Contact [18.42230728589117]
  We develop a quantum kernel-aligned regressor (QKAR) combining a shallow Pauli-Z feature map with a trainable quantum kernel alignment layer.<n>QKAR consistently outperforms classical baselines across multiple metrics.<n>These findings suggest that carefully constructed QML models could provide predictive advantages in data-constrained semiconductor modeling.
  arXiv  Detail & Related papers  (2024-09-17T00:44:49Z)
• A Quantum Approach to Synthetic Minority Oversampling Technique (SMOTE) [1.5186937600119894]
  The paper proposes the Quantum-SMOTE method to solve the prevalent problem of class imbalance in machine learning datasets. Quantum-SMOTE generates synthetic data points using quantum processes such as swap tests and quantum rotation. The approach is tested on a public dataset of Telecom Churn to determine its impact along with varying proportions of synthetic data.
  arXiv  Detail & Related papers  (2024-02-27T10:46:36Z)
• QKSAN: A Quantum Kernel Self-Attention Network [53.96779043113156]
  A Quantum Kernel Self-Attention Mechanism (QKSAM) is introduced to combine the data representation merit of Quantum Kernel Methods (QKM) with the efficient information extraction capability of SAM. A Quantum Kernel Self-Attention Network (QKSAN) framework is proposed based on QKSAM, which ingeniously incorporates the Deferred Measurement Principle (DMP) and conditional measurement techniques. Four QKSAN sub-models are deployed on PennyLane and IBM Qiskit platforms to perform binary classification on MNIST and Fashion MNIST.
  arXiv  Detail & Related papers  (2023-08-25T15:08:19Z)
• ClusterQ: Semantic Feature Distribution Alignment for Data-Free Quantization [111.12063632743013]
  We propose a new and effective data-free quantization method termed ClusterQ. To obtain high inter-class separability of semantic features, we cluster and align the feature distribution statistics. We also incorporate the intra-class variance to solve class-wise mode collapse.
  arXiv  Detail & Related papers  (2022-04-30T06:58:56Z)
• When BERT Meets Quantum Temporal Convolution Learning for Text Classification in Heterogeneous Computing [75.75419308975746]
  This work proposes a vertical federated learning architecture based on variational quantum circuits to demonstrate the competitive performance of a quantum-enhanced pre-trained BERT model for text classification. Our experiments on intent classification show that our proposed BERT-QTC model attains competitive experimental results in the Snips and ATIS spoken language datasets.
  arXiv  Detail & Related papers  (2022-02-17T09:55:21Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.