In increasingly complex digital environments, Intrusion Detection Systems (IDS) are essential for maintaining network security. Traditional machine learning techniques face challenges, including difficulties with high-dimensional data and performance issues when dealing with unbalanced datasets. Although Generative Adversarial Networks (GANs) provide an acceptable alternative by improving data creation, their traditional implementations are computationally demanding and have limitations when it comes to examining intricate data distributions. Quantum GANs (QGANs) use the benefits of quantum computing to solve these problems. Therefore, a distributed approach improves load balancing and mitigate the impact of quantum noise for NISQ devices. The proposed model efficiently learns the distribution of benign data by employing a federated hybrid QGAN architecture that combines a quantum generators with a classical discriminators. This allows the model to produce samples that closely mimic patterns found in real data. The generator in the proposed QGAN is a variational quantum circuit (VQC), and the discriminator is a neural network. Tested on the NSL-KDD dataset, the federated QGAN offers a promising option for next-generation IDS.
In increasingly complex digital environments, Intrusion Detection Systems (IDS) are essential for maintaining network security. Traditional machine learning techniques face challenges, including difficulties with high-dimensional data and performance issues when dealing with unbalanced datasets. Although Generative Adversarial Networks (GANs) provide an acceptable alternative by improving data creation, their traditional implementations are computationally demanding and have limitations when it comes to examining intricate data distributions. Quantum GANs (QGANs) use the benefits of quantum computing to solve these problems. Therefore, a distributed approach improves load balancing and mitigate the impact of quantum noise for NISQ devices. The proposed model efficiently learns the distribution of benign data by employing a federated hybrid QGAN architecture that combines a quantum generators with a classical discriminators. This allows the model to produce samples that closely mimic patterns found in real data. The generator in the proposed QGAN is a variational quantum circuit (VQC), and the discriminator is a neural network. Tested on the NSL-KDD dataset, the federated QGAN offers a promising option for next-generation IDS.
Intrusion Detection Systems (IDS) are vital for securing modern networks, but traditional machine learning-based anomaly detection methods often struggle with high-dimensional and imbalanced data. Although classical GANs help improve data generation, they are computationally intensive and limited in scalability. Quantum GANs (QGANs) present a promising alternative, but existing approaches are tested only in simulations. Instead, using real devices presents challenges not only because of the impact of quantum noise but, more significantly, due to usage restrictions imposed by their high cost. To cope with this, we propose a lightweight but effective hybrid QGAN for network intrusion detection, adopting a hybrid training approach by switching between an IBM real quantum device and its noisy simulator version, balancing computational feasibility and relevance in the real world. The results show that our model performs consistently, with minimal performance loss on real hardware.
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.
Abstract Generative adversarial networks (GANs) are a machine learning framework comprising a generative model for sampling from a target distribution and a discriminative model for evaluating the proximity of a sample to the target distribution. GANs exhibit strong performance in imaging or anomaly detection. However, they suffer from training instabilities, and sampling efficiency may be limited by the classical sampling procedure. We introduce variational quantum–classical Wasserstein GANs (WGANs) to address these issues and embed this model in a classical machine learning framework for anomaly detection. Classical WGANs improve training stability by using a cost function better suited for gradient descent. Our model replaces the generator of WGANs with a hybrid quantum–classical neural net and leaves the classical discriminative model unchanged. This way, high-dimensional classical data only enters the classical model and need not be prepared in a quantum circuit. We demonstrate the effectiveness of this method on a credit card fraud dataset. For this dataset our method shows performance on par with classical methods in terms of the F 1 score. We analyze the influence of the circuit ansatz, layer width and depth, neural net architecture parameter initialization strategy, and sampling noise on convergence and performance.
This paper investigates the application of Quantum Generative Adversarial Networks (QGANs) to data augmentation for machine learning. Traditional Generative Adversarial Networks (GANs) rely on classical neural networks, while this research explores leveraging quantum circuits as generators within a QGAN architecture. The core idea is to train a quantum generator to produce synthetic data samples, supplementing existing datasets with novel instances. The discriminator remains a classical neural network, tasked with distinguishing between real and generated data. We present a theoretical framework for this approach, outlining the mathematical formulation and key components. Specifically, we introduce the notation (x, s_1, s_2, ..., s_K) to represent data samples, generator states, and relevant parameters. The objective function for the QGAN is defined as minimizing the Jensen-Shannon divergence between the real and generated data distributions. The effectiveness of this approach is discussed, highlighting potential advantages and challenges. This work contributes to the emerging field of quantum machine learning and offers a novel strategy for improving the performance of machine learning models, particularly in scenarios with limited training data.
One of the most promising applications in the era of Noisy Intermediate-Scale Quantum (NISQ) computing is quantum generative adversarial networks (QGANs), which offer significant quantum advantages over classical machine learning in various domains. However, QGANs suffer from mode collapse and lack explicit control over the features of generated outputs. To overcome these limitations, we propose InfoQGAN, a novel quantum-classical hybrid generative adversarial network that integrates the principles of InfoGAN with a QGAN architecture. Our approach employs a variational quantum circuit for data generation, a classical discriminator, and a Mutual Information Neural Estimator (MINE) to explicitly optimize the mutual information between latent codes and generated samples. Numerical simulations on synthetic 2D distributions and Iris dataset augmentation demonstrate that InfoQGAN effectively mitigates mode collapse while achieving robust feature disentanglement in the quantum generator. By leveraging these advantages, InfoQGAN not only enhances training stability but also improves data augmentation performance through controlled feature generation. These results highlight the potential of InfoQGAN as a foundational approach for advancing quantum generative modeling in the NISQ era.
This paper investigates the application of Quantum Generative Adversarial Networks (QGANs) for the generation of synthetic data. Traditional Generative Adversarial Networks (GANs) often face challenges in producing high-quality synthetic data, especially when dealing with complex data distributions. We propose a novel framework utilizing quantum circuits to construct both the generator and discriminator within a QGAN architecture. The core innovation lies in harnessing quantum superposition and interference during the adversarial training process to potentially improve the realism and diversity of the generated synthetic data. Our approach introduces a theoretical foundation and a conceptual design for QGANs, paving the way for future research and development in this emerging field. The primary goal is to demonstrate the potential benefits of incorporating quantum mechanical principles into the GAN framework, leading to enhanced synthetic data generation capabilities. The performance of a QGAN is expected to be superior to a classical GAN due to the unique properties of quantum systems.