Managing expanding urbanisation, energy use, environmental preservation, citizen eco- nomic and living standards, and people’s ability to effectively use and adopt modern infor- mation and communication technology (ICT) are all objectives of smart cities. A branch of machine intelligence engineering known as explaining artificial intelligence (XAI) makes complex techniques approachable and adaptable for efficient decision-making in the sci- ences and technologies. The quantum uncertainty problem may be applied to the network state, which consists of several states and dimensions and requires real-time information. Specifically pertinent are the linkages between the emerging paradigms of machine learn- ing (ML), quantum computing (QC), and quantum machine learning (QML), and tradi- tional communication networks. This study provides a new method in explainable deep learning for analysing healthcare data in multimedia for long-term quantum photonic applications. Input has been collected from multimedia healthcare data and processed for noise removal and normalization.