Quantum machine learning for smart healthcare applications

Arnav Shailesh Sonavane, Shweta Jaiswar, Maitri Mistry +2 more

A. R. Deepti, Farzeen Basith

The convergence of quantum computing and artificial intelligence is creating new opportunities for smart healthcare, particularly in medical image analysis, privacy-preserving data management, and clinical decision support. This chapter presents a conceptual and architectural overview of quantum machine learning (QML) for a multidisciplinary audience. It introduces quantum computing fundamentals and discusses quantum kernel methods and variational quantum circuits using the MedMNIST benchmark. A geometric separation diagnostic is proposed as a conceptual, computationally inexpensive, hardware-independent screening construct for indicating whether quantum encoding places data in a label-separable configuration, with its empirical validation identified as future work. The chapter further examines privacy-preserving healthcare through federated learning, synthetic data governance, differential privacy, post-quantum cryptography, and regulatory considerations, and presents a five-layer quantum-enhanced healthcare architecture together with key challenges and future research directions.

Lakshita Aggarwal, Puneet Goswami

Sonam Gupta, P. C. Gupta

At the intersection of quantum computing and deep learning, the future of medical image analysis in smart healthcare ecosystems is being reshaped. In this chapter, hybrid quantum-classical deep learning (HQC-DL) architectures that combine variational quantum circuits, quantum convolutional layers, and quantum attention mechanisms with classical neural networks are discussed extensively for solving radiology, pathology, and ophthalmology problems involving high-dimensional and data-intensive tasks. We examine the theoretical foundations of quantum machine learning (QML) that are pertinent to imaging, and we review architectural models such as quantum-enhanced convolutional neural networks (QCNNs), quantum generative adversarial networks (QGANs), and quantum transformers and their applications in the context of IoMT. Current issues—such as decoherence, barren plateaus, encoding overhead, and regulatory compliance—are explored, along with the new ways of mitigating them.

Khaldun Pichlerová

Quantum Machine Learning (QML) has emerged as a transformative paradigm that integrates the computational advantages of quantum computing with the adaptive intelligence of machine learning to solve complex optimization problems in smart computing systems. Traditional optimization techniques often struggle with high-dimensional search spaces, combinatorial complexity, computational overhead, and scalability limitations in applications such as intelligent transportation, healthcare analytics, smart manufacturing, cybersecurity, cloud computing, and Internet of Things (IoT) infrastructures. QML approaches exploit quantum superposition, entanglement, and quantum parallelism to accelerate optimization tasks and enhance predictive performance beyond classical machine learning capabilities. This research proposes a hybrid Quantum Machine Learning optimization framework that combines Variational Quantum Circuits (VQCs), Quantum Neural Networks (QNNs), and classical deep learning models to address resource allocation, task scheduling, energy optimization, and intelligent decision-making in smart computing environments. The proposed framework integrates quantum-enhanced feature extraction, adaptive optimization layers, and reinforcement-based learning mechanisms to improve convergence speed and computational efficiency. Experimental analysis is performed using benchmark optimization datasets and simulated quantum environments to evaluate accuracy, execution time, energy efficiency, and scalability. Comparative results demonstrate that the proposed QML-based framework achieves superior optimization accuracy, reduced computational latency, and improved resource utilization when compared with traditional machine learning and classical metaheuristic optimization methods. Furthermore, the study highlights the practical significance of hybrid quantum-classical architectures in next-generation intelligent systems where large-scale optimization and real-time decision support are critical. The findings suggest that QML can significantly contribute to the advancement of sustainable, adaptive, and high-performance smart computing ecosystems while opening new directions for future quantum-aware intelligent infrastructures.