Quantum-Enhanced Deep Learning for Predictive Diagnostics and Personalized Treatment in Smart Healthcare Systems

The synergy between quantum computing and deep learning holds revolutionary promise in facilitating smart healthcare by addressing problems in predictive diagnosis, realtime patient tracking, and personalized therapy. Deep learning algorithms such as CNNs and RNNs have scalability and efficiency limitations in processing clinical data with high dimensionality. Their limitations can be addressed by quantum computing through an inbuilt parallelism. This work suggests that Quantum-Enhanced Deep Learning for Smart Healthcare (QEDL-Health) should marry classical deep learning with quantum algorithms to revamp paradigms in healthcare. QEDL-Health applies quantum neural networks (QNNs) with quantum gates (Pauli-X, Hadamard, CNOT) to perform encoding and feature extraction efficiently. Model parameters are trained by quantum variational circuits (QVCs), while quantum support vector machines (QSVMs) diagnose diseases with better accuracy. IoT-generated vitals in real-time tracking are processed by quantum principal component analysis (QPCA), while personalized treatment is created by quantum approximate optimization algorithms (QAOAs). The system applies quantum Fourier transforms (QFTs) and Grover’s search algorithm to process efficiently. Experimental results in MIMIC-III, TCGA, and COVID-19 datasets confirm dominance by improving diagnostic correctness by 15% while computation time is decreased by 40% in genomic data processing compared to classical methods. Experimental results confirm that comparative evaluation establishes that conventional methods in deep learning have scalability limitations in efficiency and performance in intricate tasks in disease detection (such as cancer) and in tracking in real-time. This work bridges quantum computing with AI to lay the foundations for developing efficient, scalable, and intelligent healthcare systems to detect diseases earlier in time to reduce costs and improve patient outcomes.