On the generalization limits of quantum generative adversarial networks with pure state generators

Area of Science:

• Quantum computing
• Artificial intelligence
• Machine learning

Background:

• Quantum generative adversarial networks (QGANs) are an emerging area of research.
• Investigating fully quantum implementations of QGANs for image generation is crucial.

Purpose of the Study:

• To evaluate the image generation capabilities of QGANs.
• To analyze the generalization performance of current QGAN architectures.
• To provide a theoretical explanation for observed limitations.

Main Methods:

• Extensive numerical testing of QGAN architectures.
• Analysis of fully quantum generator and discriminator implementations.
• Analytical derivation of a lower bound for discriminator quality for pure-state generator outputs.

Main Results:

• QGANs demonstrate significant challenges in generalizing across datasets.
• Models tend to converge on the average representation of training data.
• A theoretical lower bound for discriminator quality was derived, explaining performance limitations.

Conclusions:

• Current QGANs face fundamental challenges in generalization for image generation.
• The findings have implications for the development of advanced quantum generative models.
• Further research is needed to overcome these generalization barriers.