发表机构
Shanghai Maritime University; Agency for Science, Technology and Research (A*STAR); Singapore Institute of Technology; Singapore Management University (SMU); Tongji University; Tsinghua University（上海海事大学; 新加坡科技研究局; 新加坡理工学院; 新加坡管理大学; 同济大学; 清华大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出CoQui量子隐式生成对抗网络，通过坐标条件隐式函数学习解决现有QGAN的局限，在更少量子比特下于基准数据集实现优于FRQI、PQWGAN及经典基线的图像生成质量。
AI 中文摘要
量子生成对抗网络（QGANs）因使用参数化量子电路进行图像生成而受到越来越多的关注。现有的基于振幅的方法面临两个关键局限：像素位置通常由计算基索引或地址量子比特编码，导致量子资源随图像分辨率增长；同时，从归一化量子态联合解码多个像素会在像素间引入概率竞争，限制了精确的像素级控制。为解决这些问题，我们将量子图像生成重新表述为坐标条件隐式函数学习。我们的方法以空间坐标和潜在变量为输入，使用经典嵌入网络生成依赖于输入的电路参数，并在每个坐标处评估变分量子电路。像素强度直接从专用颜色量子比特的期望值获得，通过查询所有空间坐标生成完整图像。该设计将图像分辨率与地址量子比特需求解耦，避免了像素间共享的概率归一化约束。我们还设计了一种专门的变分量子电路，为坐标条件生成提供结构归纳偏置。在两个基准数据集上的模拟实验表明，我们的方法在视觉和定量质量上优于基于FRQI的生成方法和PQWGAN，同时使用更少的量子比特，且比对应的经典基线实现了更好的生成质量。
英文摘要
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.
