PROBLEMS
AI models require large and diverse training datasets, especially for specialized scenarios in automotive and aerospace applications. Acquiring a wide range of images for various conditions (e.g., different lighting, weather, or environments) can be expensive, time-consuming, and sometimes impractical. Complex image transformations often require more than simple modifications to existing images.
SOLUTIONS
Our Quantum Generative Adversarial Network (QGAN) algorithm generates artificial images based on existing image datasets. This allows for the creation of diverse, synthetic images that represent a variety of scenarios and conditions.
BENEFITS
This method can significantly reduce the cost and time for acquiring specialized image data. It increases the diversity in training datasets for AI models, bringing the industry closer to more robust and accurate AI systems for various operational conditions. The ability to generate artificial images for multiple scenarios enhances the versatility and applicability of AI models in automotive and aerospace sectors.