Quantum Image

Quantum image processing (QIP) aims to leverage quantum computing's unique capabilities for faster and more efficient image manipulation and analysis. Current research focuses on developing robust quantum algorithms for tasks like image generation (using quantum generative adversarial networks), denoising (employing machine learning models and quantum annealing), and segmentation, often utilizing novel quantum image representations like NEQR and FRQI. Overcoming limitations posed by noise and the limited availability of qubits remains a central challenge, with ongoing efforts to improve image quality and reduce computational costs. The field holds potential for advancements in various applications, including medical imaging and secure image transmission, though practical applications are still nascent.