arXiv subjects

ShiZheng Jia

Publications and source records attributed to ShiZheng Jia.

3 recordsLinked to original sources

arXiv2026

Quantum Fidelity Landscape-Guided Prior Calibration for Single-Circuit QGAN Image Generation

Quantum Generative Adversarial Networks (QGANs) have emerged as representative generative models in the Noisy Intermediate-Scale Quantum (NISQ) era and have attracted increasing attention in quantum machine learning. However, most existing QGAN methods rely on patch-based decomposition strategies, which weaken the global consistency of generated images and increase quantum resource overhead. In this work, we investigate a simpler approach: pixel-level, end-to-end image generation using a single-quantum-circuit QGAN. By analyzing the structural matching relationship between the quantum prior and the target data distribution in Hilbert space, we provide a new theoretical perspective for understanding the training behavior of naive end-to-end QGANs. Specifically, we introduce the Quantum Fidelity Landscape (QFL), defined as the pairwise-fidelity structure induced by an ensemble of quantum states and preserved under shared unitary transformations of the quantum generation process. We show that, under a fixed Lipschitz readout, this invariant imposes a one-sided bound on decoded sample separation, motivating calibration of the prior-induced QFL before adversarial training. To validate this theoretical insight, we propose BasicQGAN, a QGAN framework incorporating quantum prior calibration. Before adversarial optimization, BasicQGAN aligns the prior-induced QFL with the data-induced QFL. Experimental results on small-scale grayscale image datasets show that BasicQGAN achieves stable and effective end-to-end pixel-level image generation while requiring fewer qubits and trainable parameters than representative patch-based quantum generators. Furthermore, experiments with different initial quantum-state ensembles show that QFL-calibrated ensembles achieve better generative performance.

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arXiv2026

CoQui: A Coordinate-Conditioned Quantum Implicit Generative Adversarial Network for End-to-End Image Generation

Quantum generative adversarial networks (QGANs) have attracted increasing attention for image generation using parameterized quantum circuits. Existing amplitude-based approaches face two key limitations: pixel locations are typically encoded by computational-basis indices or address qubits, causing quantum resources to grow with image resolution; meanwhile, jointly decoding many pixels from normalized quantum states introduces probability competition among pixels and limits precise pixel-wise control. To address these issues, we reformulate quantum image generation as coordinate-conditioned implicit function learning. Our method takes spatial coordinates and latent variables as inputs, uses a classical embedding network to generate input-dependent circuit parameters, and evaluates a variational quantum circuit at each coordinate. Pixel intensities are directly obtained from the expectation value of a dedicated color qubit, and a complete image is generated by querying all spatial coordinates. This design decouples image resolution from address-qubit requirements and avoids shared probability-normalization constraints across pixels. We further design a specialized variational quantum circuit to provide structural inductive bias for coordinate-conditioned generation. Simulated experiments on two benchmark datasets show that our method outperforms FRQI-based generation and PQWGAN in visual and quantitative quality while using fewer qubits, and also achieves better generation quality than the corresponding classical baseline.

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arXiv2024

iHQGAN: A Lightweight Invertible Hybrid Quantum-Classical Generative Adversarial Network for Unsupervised Image-to-Image Translation

Leveraging quantum computing's intrinsic properties to enhance machine learning has shown promise, with quantum generative adversarial networks (QGANs) demonstrating benefits in data generation. However, the application of QGANs to complex unsupervised image-to-image (I2I) translation remains unexplored. Moreover, classical neural networks often suffer from large parameter spaces, posing challenges for GAN-based I2I methods. Inspired by the fact that unsupervised I2I translation is essentially an approximate reversible problem, we propose a lightweight invertible hybrid quantum-classical unsupervised I2I translation model - iHQGAN, by harnessing the invertibility of quantum computing. Specifically, iHQGAN employs two mutually approximately reversible quantum generators with shared parameters, effectively reducing the parameter scale. To ensure content consistency between generated and source images, each quantum generator is paired with an assisted classical neural network (ACNN), enforcing a unidirectional cycle consistency constraint between them. Simulation experiments were conducted on 19 sub-datasets across three tasks. Qualitative and quantitative assessments indicate that iHQGAN effectively performs unsupervised I2I translation with excellent generalization and can outperform classical methods that use low-complexity CNN-based generators. Additionally, iHQGAN, as with classical reversible methods, reduces the parameter scale of classical irreversible methods via a reversible mechanism. This study presents the first versatile quantum solution for unsupervised I2I translation, extending QGAN research to more complex image generation scenarios and offering a quantum approach to decrease the parameters of GAN-based unsupervised I2I translation methods.

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