This study presents an effective analysis of a hybrid transfer learning algorithm in medical image processing, specifically focusing on
histopathological cancer detection. The research involves a two-part approach. First, a hybrid model was created, combining classical and quantum units for classifying images into cancerous and non-cancerous cells. Second, the performance of this hybrid model was compared with traditional models, quantum simulators like Pennylane, and IBM's real quantum computers

Predictive models face the reality of encountering various attacks from vin- dictive entities. Adversarial attacks are one of these attacks, which mainly target AI models like Deep Learning (DL) or Machine Learning (ML) mod- els. These attacks involve deliberately perturbing original input images with a carefully crafted noisy image, resulting in incorrect image classification by the model. Perturbed Images are imperceptible to the human eye, but it confuses the model, leading to misclassification. 

Intelligent governance and responsible AI are imperative pillars in navigating the rapidly evolving landscapes of classical and quantum computing. In classical computing, intelligent governance entails robust frameworks that ensure AI systems are designed, deployed, and managed ethically, transparently, and accountably. This involves addressing issues such as algorithmic bias, data privacy, and the societal impacts of AI technologies. Similarly, in the realm of quantum computing, where the potential of AI is vast and transformative, responsible AI practices are paramount.