The exponential growth of healthcare data and the increasing complexity of medical diagnostics demand innovative computational approaches that can address the limitations of traditional techniques. This paper presents a novel quantum computing framework optimized for smart healthcare applications. The proposed framework leverages quantum principles, such as superposition and entanglement, to enhance computational efficiency, scalability, and accuracy in healthcare-related tasks, including disease diagnosis, real-time patient monitoring, and personalized medicine. A comparative analysis was conducted between the proposed framework and traditional methods, including Classical Machine Learning, Deep Learning, and Optimization Algorithms, across five key performance metrics: accuracy, processing speed, scalability, reliability, and resource efficiency. Experimental results demonstrate that the quantum framework consistently outperforms existing techniques, achieving up to 15% higher accuracy and 70% faster processing times in high-dimensional and real-time scenarios. This research highlights the potential of quantum computing to revolutionize healthcare by addressing critical challenges, such as handling large-scale data, achieving real-time responsiveness, and optimizing complex medical decision-making processes. The findings underscore the transformative potential of quantum technologies to redefine data-driven healthcare, paving the way for more efficient and effective patient care.