Topic
gan
Controlled Benchmark Finds No Quantum Advantage in Brain MRI Data Augmentation
A controlled benchmark study by Haider and Figini shows that quantum-latent GAN augmentation does not improve brain MRI classification over real-data-only training or classical GANs. The quantum and classical generators were statistically indistinguishable across all data fractions from 5% to 100%.
QC-GAN: Parameter-Efficient Speech Enhancement Model Delivers High Fidelity with 0.89M Parameters
A new speech enhancement framework, QC-GAN, combines a Quaternion Conformer generator with MetricGAN-based training to deliver state-of-the-art perceptual quality using remarkably few parameters. The model achieves a PESQ score of 3.48 with only 0.89M parameters, and a 35K-parameter variant reaches 3.23, outperforming conventional methods at a fraction of the size. This parameter efficiency makes it suitable for edge deployment in voice-controlled systems, including logistics and supply chain applications.