Data scarcity and sparsity in bio-manufacturing poses challenges for accurate model development, process monitoring, and optimization. We aim to replicate and capture the complex dynamics of industrial bioprocesses by proposing the use of a Quantum Wasserstein Generative Adversarial Network with Gradient Penalty (QWGAN-GP) to generate synthetic time series data for industrially relevant processes. The generator within our GAN is comprised of a Parameterized Quantum Circuit (PQC). This methodology offers potential advantages in process monitoring, modeling, forecasting, and optimization, enabling more efficient bioprocess management by reducing the dependence on scarce experimental data. Our results demonstrate acceptable performance in capturing the temporal dynamics of real bioprocess data. We focus on Optical Density, a key measurement for Dry Biomass estimation. The data generated showed high fidelity to the actual historical experimental data. This intersection of quantum computing and machine learning has opened new frontiers in data analysis and generation, particularly in computationally intensive fields, for use cases such as increasing prediction accuracy for soft sensor design or for use in predictive control.
Data scarcity and sparsity in bio-manufacturing poses challenges for accurate model development, process monitoring, and optimization. We aim to replicate and capture the complex dynamics of industrial bioprocesses by proposing the use of a Quantum Wasserstein Generative Adversarial Network with Gradient Penalty (QWGAN-GP) to generate synthetic time series data for industrially relevant processes. The generator within our GAN is comprised of a Parameterized Quantum Circuit (PQC). This methodology offers potential advantages in process monitoring, modeling, forecasting, and optimization, enabling more efficient bioprocess management by reducing the dependence on scarce experimental data. Our results demonstrate acceptable performance in capturing the temporal dynamics of real bioprocess data. We focus on Optical Density, a key measurement for Dry Biomass estimation. The data generated showed high fidelity to the actual historical experimental data. This intersection of quantum computing and machine learning has opened new frontiers in data analysis and generation, particularly in computationally intensive fields, for use cases such as increasing prediction accuracy for soft sensor design or for use in predictive control.
Anomaly detection in time series is constrained by the scarcity and unpredictability of real anomalous events. Classical supervised and generative approaches struggle to capture the statistical complexity of rare, high-impact anomalies while maintaining realistic normal dynamics. This work introduces a Quantum Wasserstein Generative Adversarial Network (QWGAN) for synthetic anomaly generation that separates the modeling of normal and anomalous behaviors using quantum parameterized circuits. Leveraging quantum superposition and entanglement, the QWGAN achieves a richer generative capacity with an order-of-magnitude reduction in parameters compared to classical counterparts. The model is trained and evaluated on cyber security datasets, demonstrating lower Earth Mover’s Distance and volatility error while preserving temporal structure and distributional fidelity. A windowed synthesis mechanism enables controlled injection of variable-length point and contextual anomalies. Our results demonstrate that QWGAN provides a scalable, parameter-efficient approach for generating high-fidelity synthetic anomalous time series data to enable effective benchmarking and augmentation of anomaly detection models under limited anomaly data.
Synthetic data generation has proven to be a promising solution for addressing data availability issues in various domains. Even more challenging is the generation of synthetic time series data, where one has to preserve temporal dynamics, i.e., the generated time series must respect the original relationships between variables across time. Recently proposed techniques such as generative adversarial networks (GANs) and quantum-GANs lack the ability to attend to the time series specific temporal correlations adequately. We propose using the inherent nature of quantum computers to simulate quantum dynamics as a technique to encode such features. We start by assuming that a given time series can be generated by a quantum process, after which we proceed to learn that quantum process using quantum machine learning. We then use the learned model to generate out-of-sample time series and show that it captures unique and complex features of the learned time series. We also study the class of time series that can be modeled using this technique. Finally, we experimentally demonstrate the proposed algorithm on an 11-qubit trapped-ion quantum machine.
Abstract Quantum machine learning offers powerful advantages in processing high-dimensional data, but loading classical probability distributions into quantum states remains a major challenge. This work extends quantum generative adversarial networks (qGANs) to overcome two critical limitations of early approaches: poor robustness to noise on near-term devices and limited capacity to model complex, multi-modal distributions. We introduce hybrid qGAN architectures that combine Wasserstein GAN with gradient penalty (WGAN-GP) and maximum mean discrepancy (MMD) losses with expressive quantum circuits, including quantum convolutional neural networks (QCNNs) and EfficientSU2 ansätze. Training is performed using seamless PyTorch–Qiskit integration, enabling stable optimization on both simulators and real quantum hardware.Experiments on 2D Gaussian and log-normal distributions—directly relevant to financial modeling—show faster convergence and higher fidelity than prior qGAN designs, with up to 80% lower Wasserstein distance under 5% depolarizing noise. The framework is further validated through European call option pricing via quantum amplitude estimation (QAE), achieving pricing errors below 1% on IBM’s 20-qubit superconducting systems. Theoretical analysis confirms gradient stability under noise, while empirical results include comprehensive noise sweeps, t-SNE latent space visualizations, and rigorous statistical tests (KL divergence, KS tests). This work establishes a practical path toward quantum advantage in generative modeling, with fully open-source code for reproducibility.
Synthetic biological data play a crucial role in bioinformatics research, particularly in scenarios involving limited data availability and strict privacy constraints. However, conventional generative adversarial networks often face challenges in simultaneously preserving local statistical characteristics and global data distributions, motivating the exploration of quantum-enhanced generative models. This paper proposes and evaluates a hybrid quantum–classical generative adversarial network (QGAN) for efficient and high-quality biological data generation. The generator is implemented using a variational quantum circuit that encodes classical biological features into parameterized quantum states, while the discriminator is realized through a classical neural network architecture. The proposed framework is evaluated against classical GAN models using quantitative metrics, including the Classifier Two-Sample Test (C2ST), Maximum Mean Discrepancy (MMD) and Wasserstein Distance (WD). Experimental results show that the QGAN achieves stronger local statistical distribution matching, reflected by lower MMD values, whereas the Classical GAN demonstrates more stable global distribution alignment through lower WD. Furthermore, the analysis reveals that the stochastic sampling behavior of parameterized quantum circuits contributes to higher output variance during training, influencing large-scale distribution consistency. These findings indicate that hybrid quantum–classical generative models can effectively preserve localized statistical structures while highlighting the trade-off between local fidelity and global distribution stability, supporting their potential for future applications in genomics, drug discovery, and privacy-preserving biomedical data synthesis.
Understanding the information processing in neuronal networks relies on the development of computational models that accurately reproduce their activity data. Machine learning techniques have shown promising results in generating synthetic neuronal data, but interpretability remains an issue due to a large number of parameters requiring fitting. Quantum machine learning models, particularly quantum generative learning, are emerging as more compact alternatives that offer similar outcomes. This study presents an efficient framework for generating synthetic neuronal data using a Quantum Generative Adversarial Network (QGAN), with a quantum generator and a classical discriminator. We tested the proposed framework for the minimal case of two neurons, considering the case of single time-steps. Preliminary results demonstrate the QGAN's capability to achieve reliable outcomes with a reduced number of trainable parameters, scaling efficiently for increasing neuronal network sizes. The model effectively captures spiking frequencies of real data, although further refinement is required to incorporate temporal correlations for more extended time-steps. Despite certain limitations, this study lays the foundation for future advancements in using quantum adversarial generative networks to model neuronal activity. The promising potential of QGANs in this domain highlights the possibility of gaining valuable insights into the functioning of complex biological systems through quantum-inspired computational methods.
The need for synthetic medical data is growing rapidly as machine learning becomes more important in healthcare, especially with strict privacy rules like HIPAA in the United States and GDPR in Europe. While Generative Adversarial Networks (GANs) have shown potential in generating realistic synthetic data, they often face problems such as mode collapse, unstable training, and difficulty handling complex, high-dimensional medical datasets.In this work, we introduce a new Quantum Generative Adversarial Network (QUGAN) that combines the strengths of quantum and classical computing. The generator is built using a Variational Quantum Circuit (VQC), while the discriminator remains a classical neural network. To our knowledge, this is the first time a QUGAN has been applied to structured tabular healthcare data and especially using a VQC circuit in the generator, which makes up most real electronic health records but is rarely explored in quantum machine learning. The model was developed step by step, starting from a simple GAN baseline, and gradually improved into a more powerful hybrid system.The QUGAN performed considerably better compared with the standard baseline when tested on actual cardiovascular dataset. While achieving synthetic precision scores of up to 92%, it decreased the Maximum Mean Discrepancy (MMD) by 50.6%, the Kullback–Leibler (KL) divergence by 65.6%, and the Jensen–Shannon (JS) divergence by 67.7%. These results indicate that the model creates diverse samples close to real data, which are a key factor for safe data sharing and dependable analysis later. In short, our findings suggest that QUGANs that uses VQC circuits can be reliable method to produce tabular medical datasets.