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This paper introduces CoQui, a novel coordinate-conditioned quantum generative adversarial network (QGAN) that reformulates image generation by leveraging implicit function learning. By decoupling image resolution from address-qubit requirements and avoiding shared probability-normalization constraints, CoQui enhances pixel-wise control and reduces quantum resource demands. Experimental results demonstrate that CoQui outperforms existing QGAN methods and classical baselines in both visual and quantitative quality while utilizing fewer qubits.
CoQui achieves superior image generation quality with fewer qubits by decoupling pixel control from quantum resource constraints.
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.
