CoQui is a coordinate-conditioned quantum implicit GAN that reads each pixel from a dedicated color qubit's expectation value, decoupling qubit count from image resolution and reporting improved generation over amplitude-based quantum GAN baselines.
QFGN: A Quantum Approach to High-Fidelity Implicit Neural Representations
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abstract
Implicit neural representations have shown potential in various applications. However, accurately reconstructing the image or providing clear details via image super-resolution remains challenging. This paper introduces Quantum Fourier Gaussian Network (QFGN), a quantum-based machine learning model for better signal representations. The frequency spectrum is well balanced by penalizing the low-frequency components, leading to the improved expressivity of quantum circuits. The results demonstrate that with minimal parameters, QFGN outperforms the current state-of-the-art (SOTA) models. Despite noise on hardware, the model achieves accuracy comparable to that of SIREN, highlighting the potential applications of quantum machine learning in this field.
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quant-ph 1years
2026 1verdicts
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CoQui: A Coordinate-Conditioned Quantum Implicit Generative Adversarial Network for End-to-End Image Generation
CoQui is a coordinate-conditioned quantum implicit GAN that reads each pixel from a dedicated color qubit's expectation value, decoupling qubit count from image resolution and reporting improved generation over amplitude-based quantum GAN baselines.