Comparing Quantum-Classical Techniques for Medical Domain

Abstract

One of the biggest health issues is cancer, for which early diagnosis is highly crucial to enhance chances of survival. In the initial stages of diagnosis, the differentiation between malignant and benign tumors used to rely heavily on precise and effective diagnostic measures. Traditional Machine Learning (ML) methods have exhibited great promise in predicting cancer, providing reliability and strength in handling structured and unstructured data. The growing size and sophistication of clinical data, though, have revealed limitations in traditional methods such as scalability and computer overhead.Current advancements in Quantum Circuit (QC) have introduced a new paradigm in data processing and learning, providing dramatic advantages through quantum effects such as superposition and entanglement. This work compares and examines the performance of conventional ML models, Quantum Machine Learning (QML), and hybrid Quantum-Classical Machine Learning (QCML) frameworks and delves deep into ensemble learning methods across different types of datasets for cancer classification. The hybrid approaches integrate classical feature extraction methods with quantum layers with the ability to encode features into qubits, leveraging QC’s computation power. In addition, the quantum approaches exhibit robustness and a larger ability to generalize over multiple different sample distributions. These findings highlight the increasing power of quantum enhancement in ML in enhancing diagnostic performance for complicated medical applications. Analogously, the deep learning ensemble model constructed for histopathology image interpretation shows outstanding accuracy and generalizability to numerous image datasets, thereby confirming the potency of aggregating a number of architectures to cope with heterogeneity in medical imaging. Overall, our work emphasizes the synergistic capability of hybrid QCML models and ensemble deep learning methods in tackling the enhanced complexity in medical diagnostics, especially in the early detection and classification of cancer. While the hybrid quantum-classical models prove computational efficiency with better generalization on high-dimensional biomedical data, the ensemble deep learning models, particularly in histopathological image analysis, prove to be more accurate and more robust across a range of datasets. Considered together, these strategies constitute a notable leap forward in the creation of intelligent responsive, and scalable diagnostic systems that seamlessly integrate classical, deep learning, and quantum computing paradigms for healthcare solutions ready for the future.