Abstract This work presents a Hybrid Quantum Generative Adversarial Network (HQGAN) that combines symmetry-preserving variational quantum circuits with entangling power optimization, targeting improved expressivity and training stability on NISQ devices. The quantum generator employs layered parameterized circuits composed of Ising-type entangling gates and symmetric single-qubit rotations, inspired by convolutional neural networks. To guide circuit design, we quantify the entanglement capability of each layer using the entangling power (EP) metric via Monte Carlo sampling. Compared to traditional fully connected architectures, HQGAN introduces three major improvements: (1) symmetry-preserving gates preserve translational invariance and enhance feature extraction; (2) modular subcircuit designs with patch-wise outputs reduce parameter overhead by 50\% while improving convergence; (3) optimized entanglement via EP leads to better initialization and performance. Experimental results on Fashion-MNIST and OptDigits demonstrate that HQGAN achieves the same level of fidelity as the original model within 400 epochs,The KL divergence of the entanglement-enhanced Ising-based model is reduced to 0.023, compared to the baseline model. Moreover,parameter complexity is reduced from \( O(N^2) \) to \( O(\mathrm{poly}(N)) \), alleviating overfitting and improving generalization. These results confirm the importance of structure-aware circuit design and entanglement-driven optimization for quantum generative modeling under near-term constraints.
Abstract This work presents a Hybrid Quantum Generative Adversarial Network (HQGAN) that combines symmetry-preserving variational quantum circuits with entangling power optimization, targeting improved expressivity and training stability on NISQ devices. The quantum generator employs layered parameterized circuits composed of Ising-type entangling gates and symmetric single-qubit rotations, inspired by convolutional neural networks. To guide circuit design, we quantify the entanglement capability of each layer using the entangling power (EP) metric via Monte Carlo sampling. Compared to traditional fully connected architectures, HQGAN introduces three major improvements: (1) symmetry-preserving gates preserve translational invariance and enhance feature extraction; (2) modular subcircuit designs with patch-wise outputs reduce parameter overhead by 50% while improving convergence; (3) optimized entanglement via EP leads to better initialization and performance. Experimental results on Fashion-MNIST and OptDigits demonstrate that HQGAN achieves the same level of fidelity as the original model within 400 epochs,The KL divergence of the entanglement-enhanced Ising-based model is reduced to 0.023, compared to the baseline model. Moreover,parameter complexity is reduced from ( O(N^2) ) to ( O(\mathrm{poly}(N)) ), alleviating overfitting and improving generalization. These results confirm the importance of structure-aware circuit design and entanglement-driven optimization for quantum generative modeling under near-term constraints.
Quantum Generative Adversarial Networks (QGANs) combine quantum circuit based generative models with classical discriminators to exploit quantum advantages in generative modeling. This work investigates a hybrid QGAN architecture with a quantum-enhanced generator and a classical discriminator, applied to the Modified National Institute of Standards and Technology (MNIST) dataset. We implement and benchmark three different quantum circuit ansätze - namely a basic entangler circuit, a simple two-design circuit and a strongly entangling circuit, with each QGAN model being constructed using six layers of the respective ansätze. To manage the 28x28 image complexity, the generator employs a patch wise scheme, dividing each image into small patches generated by separate sub-circuits. The performance of each ansatz is evaluated in terms of training stability and output image quality. Our results demonstrate that the choice of quantum circuit ansatz significantly impacts the QGAN’s ability to learn the distribution of handwritten digits. In particular, the strongly entangling ansatz achieves the best performance at the cost of stability and image fidelity among the three yielding recognizable digit images. While no quantum advantage was demonstrated, this study provides comparative analysis and insights into designing effective quantum generators for hybrid QGANs on image data, serving as a benchmark for future developments in quantum-enhanced generative models.
Hybrid Quantum Generative Adversarial Networks (HQ-GANs) have emerged as a promising class of models at the intersection of quantum computing and generative artificial intelligence. Unlike early quantum generative approaches that relied on fully quantum pipelines, hybrid QGANs combine parameterized quantum circuits with classical optimization and discrimination mechanisms, enabling practical training under the constraints of noisy intermediate-scale quantum (NISQ) hardware. This paper presents a comprehensive technical review of hybrid QGAN architectures, with a particular focus on implicit quantum representations and hybrid optimization strategies that enhance expressivity, stability, and scalability. We analyze how recent advances in variational circuit design, data re-encoding, Wasserstein-based objectives, and hybriddepth control have reshaped the feasibility of quantum generative learning. New mathematical formulations are introduced to describe implicit quantum mappings and hybrid adversarial convergence. Comparative analysis highlights performance trends, architectural trade-offs, and unresolved challenges related to noise, barren plateaus, and hardware–software mismatch. The paper concludes by outlining future research directions toward robust, application-ready quantum generative intelligence.
The class imbalance of electrocardiogram (ECG) data is a serious impediment to the development of diagnostic systems for heart disease. To address this issue, this paper proposes HQ-DCGAN, a hybrid quantum deep convolutional generative adversarial network, specifically designed for the generation of ECGs. The proposed algorithm employs different quantum convolutional layers for the generator and discriminator as feature extractors and utilizes parameterized quantum circuits (PQCs) to enhance computational capabilities, along with the model-feature mapping process. Moreover, this algorithm preserves the nonlinearity and scalability inherent to classical convolutional neural networks (CNNs), thereby optimizing the utilization of quantum resources, and ensuring compatibility with contemporary quantum devices. In addition, this paper proposes a novel evaluation metric, 1D Fréchet Inception Distance (1DFID), to assess the quality of the generated ECG signals. Simulation experiments show that HQ-DCGAN exhibits strong performance in ECG signal generation. Furthermore, the generated signals achieve an average classification accuracy of 82.2%, outperforming the baseline algorithms. It has been experimentally proven that HQ-DCGAN is friendly to currently noisy intermediate-scale quantum (NISQ) computers, in terms of both number of qubits and circuit depths, while improving the stability.
Generative Adversarial Networks (GANs) have demonstrated immense potential in synthesizing diverse and high-fidelity images. However, critical questions remain unanswered regarding how quantum principles might best enhance their representational and computational capacity. In this paper, we investigate hybrid quantum-classical GAN architectures supplemented by transfer learning to systematically examine whether incorporating Variational Quantum Circuits (VQCs) into the generator, the discriminator, or both improves performance over a fully classical baseline. Our findings indicate that fully hybrid models, which incorporate VQCs in both the generator and the discriminator, produce images with higher quality and achieve more favorable quantitative metrics compared to their fully classical counterparts. In particular, placing the quantum block in the generator appears to accelerate the early emergence of visual structure, whereas placing it in the discriminator slows early visual convergence but improves the final quantitative quality metric. Incorporating quantum blocks into both networks yields the strongest overall performance. Moreover, the model sustains comparable performance even when the dataset size is reduced. Overall, the results underscore that carefully integrating quantum computing with classical adversarial training and pretrained feature extraction can enrich GAN-based image synthesis. These insights open avenues for future work on higher-resolution tasks, alternative quantum circuit designs, and experimentation with emerging quantum hardware.
This paper introduces a novel hybrid quantumclassical model using QGAN (quantum generative adversarial network) to achieve more efficient, convergent and noiserobust machine learning for nearterm quantum (NISQ) applications. Unlike the existing GAN type models that were purely classical or purely quantum, to propose a quantum generator consisting of shallow variation circuits (48 qubits) together with a classical discriminator that is crucial for the stability of adversarial training. The empirical results on the MNIST dataset showed that were able to perform classification with an accuracy of 94.2%, an improvement of over 3.2% from a straightforward classical only approach, while NVGAN also improved its generative performance to a Fréchet Inception Distance (FID) score of 38.7 or 26% in quality enhancement from synthesis perspective. Both trainings converged 50 epochs earlier than each training when used the hybrid model (30% improvement in convergence). The key innovation of this work is bringing (1) to bear both the utility of the quantumenhanced feature representation to have at disposal, and (2) classical stability measures and erroraware optimizations (such as parameter clipping, early stopping), such that its hybridization enabled it to continue operating with>90%accuracy under cases of reasonable depolarizing/amplitude damping noise on an MLP where purely quantum formulations decayed to (approx 70%). Altogether, the paper validates the presented hybrid QGAN approach as a statistically substantiated noise robust and scalable representational tool for real world applications of quantum enhanced machine learning tasks on NISQ platforms.
This paper proposes and evaluate Hybrid Quantum Convolutional Neural Networks (HQCNNs) that integrate Structured Variational Quantum Circuits (SVQCs) into classical convolutional networks for image classification. Prior quanvolutional architectures often rely on unstructured variational circuits, leading to barren-plateau training landscapes, unpredictable convergence, and poor scaling of expressivity as qubit count grows. Moreover, simplistic data-encoding schemes can either fail to capture spatial correlations or demand impractically deep circuits, while arbitrary entanglement patterns obscure the relationship between circuit design and learning capacity. To address these challenges, we design four HQCNN architectures with progressively increased quantum encoding precision, entanglement topology, and classical post-processing depth. Each HQCNN replaces the convolutional layer with a quanvolutional layer: local image patches are encoded into small qubit registers via an Enhanced Novel Enhanced Quantum Representation (ENEQR), then processed by the SVQCs, and then measured to produce quantum feature maps. The four versions vary in quantum structure: Version 1 uses a simple linear 5-qubit chain (q=2), Version 2 adds wrap-around entanglement (closed-loop topology), Version 3 augments the classical head with a deeper batch-normalized multilayer perceptron, and Version 4 increases encoding fidelity to q=4 (7 qubits per patch). All models end-to-end using the Adam optimizer and cross-entropy loss on the MNIST dataset for 10 epochs using a PennyLane simulator. Version 4 achieves the best test accuracy (96% on MNIST). Fourier spectrum analysis reveals that richer entanglement and higher-fidelity encoding broaden the circuits’ harmonic spectrum, correlating with enhanced representational capacity and improved classification performance. These results confirm that carefully structured quantum layers and enhanced encoding can enrich feature representations and boost accuracy, albeit with increased simulation overhead and training complexity.