Authors: Runqiu Shu, Xusheng Xu, Man-Hong Yung, Wei Cui

Published on: February 02, 2024

Impact Score: 8.38

Arxiv code: Arxiv:2402.01791

Summary

• What is new: The introduction of a hybrid quantum-classical architecture to improve GAN performance, which is more efficient in terms of computational resources.
• Why this is important: Training GANs is computationally expensive, especially for large neural networks.
• What the research proposes: A hybrid quantum-classical architecture (QC-GAN) that combines a quantum variational circuit with a one-layer neural network for the generator, and a traditional neural network for the discriminator.
• Results: QC-GAN achieved better performance metrics than classical GANs with fewer training parameters and iterations needed for convergence.

Technical Details

Technological frameworks used: MindSpore Quantum

Models used: Quantum variational circuit, one-layer neural network

Data used: Hand-written image generation

Potential Impact

Companies involved in image, video, and audio content generation could benefit. Markets related to AI and quantum computing technology development could see shifts.

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