As quantum computing continues to advance alongside machine learning, Quantum Generative Adversarial Networks (QGANs) have gained attention as a compelling generative modeling approach in quantum machine learning. Despite their potential, most existing QGAN implementations rely on manually designed quantum circuits, which often suffer from high complexity and limited scalability. To overcome these limitations, we propose a Quantum Architecture Search (QAS) method built upon the SuperCircuit framework. This approach enables the automatic discovery of efficient quantum circuit structures tailored for generative tasks, thereby reducing gate count. However, applying SuperCircuit-based QAS directly to QGANs presents two primary challenges: an expansive search space and a lack of guarantees regarding circuit efficiency. To mitigate these issues, we incorporate Principal Component Analysis (PCA) and a PCA-guided feature distribution mechanism during preprocessing. This strategy both compresses the search space and ensures balanced allocation of principal components across sub-generators. Extensive experiments confirm the viability of the proposed framework. When employing the discovered quantum circuits, QAS-QGAN achieves image quality comparable to a state-of-the-art (SOTA) baseline (Silver et al 2023 2023 IEEE/CVF Int. Conf. on Computer Vision (ICCV) pp 7007–16) on both the MNIST and Fashion MNIST datasets. Notably, our model reduces the number of two-qubit gates by 52.08% on MNIST and 52.92% on Fashion MNIST, highlighting substantial improvements in quantum resource efficiency. Furthermore, the model successfully generates high-resolution facial images ( 109 × 89 ) on the CelebA dataset, demonstrating strong scalability and practical applicability.

Quantum information hiding, as an extension of classical information hiding techniques into the realm of quantum information, currently focuses on embedding classical bits (0/1) within quantum carriers. This includes methods such as disguising classical secret information as channel noise and embedding it within quantum error correction codes. However, the embedding mechanism for arbitrary quantum states $\alpha |0\rangle + \beta |1\rangle $ is still in the exploratory stage. This paper proposes an innovative framework that leverages the redundant space of quantum error correction codes to construct a nonlinear decoding architecture with quantum neural networks. This approach simultaneously achieves both carrier state error correction and secret state embedding and extraction functions. Specifically, the [5,1,3] stabilizer code is used as the carrier, with secret state embedding achieved through single-qubit substitution, and a quantum autoencoder is designed for steganographic state information decoding. The proposed framework features fully quantum-based input/output systems, overcoming the limitations of traditional variational quantum circuits that rely on probabilistic measurements for output generation. By performing full ground-state measurements at the autoencoder bottleneck layer and optimizing the parallel sub-network architecture, the network achieves efficient convergence and effective extraction of single-copy quantum states. Experimental results show that under the conditions of optimized parameters and data size of 20, the training losses for the carrier and secret states are 0.03 and 0.08, respectively, with test fidelities of 0.92 and 0.93. For a data size of 50, the secret states recovery fidelity exceeds 0.87. KS test analysis indicates that the full ground-state measurement and parallel sub-network are key strategies for achieving network performance. Equivalent error analysis shows that this approach successfully utilizes the potential redundant space of quantum error correction codes, providing new research directions for quantum state information hiding.

Quantum generative adversarial networks (QGANs) have demonstrated strong capabilities in tasks like synthetic data generation and detecting anomalies. Recent developments have increasingly integrated traditional machine learning techniques to boost the performance of QGANs. Motivated by this progress, we propose an innovative QGAN architecture that incorporates a classical learning component and employs a dual-generator design. Our approach improves upon the traditional hybrid quantum–classical GAN structure and introduces a redesigned loss function tailored for the new model. Experiments on multiple datasets indicate that our method surpasses previous techniques in image generation quality, achieving a 1.38% average reduction in Fréchet inception distance scores compared to the current state-of-the-art, and improvements of 6.52%, 0.36%, and 0.38% in structural similarity index, cosine similarity, and peak signal-to-noise ratio metrics, respectively. Additionally, our architecture supports the generation of larger images (up to 78 × 78 ), as verified on the CelebA dataset. Simulations conducted in noisy conditions further confirm the robustness and effectiveness of both the proposed architecture and loss function.

While classical steganography achieves maturity in digital media, hiding arbitrary quantum states (α|0 + β|1) has emerged as an intriguing frontier. To address this problem, we establish a formal model of controllable random perturbation unitaries for single/multi-stego state tasks. We progressively explore Quantum Autoencoder (QAE) structures through three stages: starting from single-state scenarios without perturbation, advancing to perturbed conditions, and finally extending to multi-state tasks. We design two perturbation-based encoding schemes using Quantum Autoencoders (QAE): the simple scheme (QAE-DD) leverages the inverse application of encoding-decoding modules, while the improved scheme (QAE-OSP) incorporates orthogonal projection routing and parallel subnetworks to restructure the hidden-layer architecture. In 3-qubit entangled-state simulations with data scales n ≤ 10 and perturbation strengths ε ∈ [0, 1], QAE-DD performs well under low perturbation, whereas QAE-OSP maintains higher fidelity between the carrier and secret states under high perturbation conditions (e.g., n = 5, ε = 0.6), with fidelity values F ρ stego , ρstego = 0.91 / F (ρ S , ρS) = 0.84 providing a reference for network design. Finally, we extend the single-carrier ("1 + 1") task to the multi-carrier ("1 + N ") scenario by constructing a "centroid" state training set based on the principal component of carrier-state groups and validating the applicability of both models. Under the conditions n = 5 and ε = 0.6, the QAE-OSP model successfully improves the average fidelity between multiple secret states and carrier states from 0.68 to 0.90, demonstrating its capability to aggregate multiple carriers to enhance overall concealment. Although the present study covers only small-scale data and networks, it lays the groundwork for a neural network framework that covertly embeds arbitrary quantum states into high-dimensional quantum states, providing a basis for future exploration.