Utilizing a Hybrid Matrix Product State and Variational Quantum Circuit Architecture for the Detection of Kidney Diseases
Vidur Reddy Jannapureddy, Shinjae Yoo, Huan–Hsin Tseng · 2024
Computed tomography (CT) scans are widely used for diagnosing and monitoring renal diseases but have a misdiagnosis rate of nearly 30%. Neural networks, particularly when integrated with quantum architectures, can significantly reduce this rate. This study introduces a Hybrid Matrix Product State (MPS) and Variational Quantum Circuit (VQC) architecture, where MPS is used for feature extraction and VQC for classification. The MPS-VQC model demonstrated enhanced performance, achieving 99.49% accuracy in distinguishing between kidney cysts, tumors, and healthy scans, compared to 52.92% using Principal Component Analysis (PCA)-VQC. As medical imaging increasingly adopts machine learning, quantum-inspired architectures like MPS-VQC offer a promising solution for improving diagnostic accuracy in renal disease detection and can be adapted for broader medical imaging tasks to enhance clinical outcomes and efficiency.