Evaluating Quantum Machine Learning Approaches for Histopathological Cancer Detection: Classical, Hybrid Simulation, and IBM Quantum Computing

Abstract

This study presents an effective analysis of a hybrid transfer learning algorithm in medical image processing, specifically focusing on histopathological cancer detection. The research involves a two-part approach. First, a hybrid model was created, combining classical and quantum units for classifying images into cancerous and non-cancerous cells. Second, the performance of this hybrid model was compared with traditional models, quantum simulators like Pennylane, and IBM's real quantum computers (Lima, Quito, Belem, and Jakarta), considering factors such as the number of qubits, Variational Quantum Classifier (VQC), and number of images. The objective was to aid users in selecting the optimal combination of classical and quantum units for accurate classification. The study found that the hybrid transfer learning model, when used with the Pennylane quantum simulator, yielded slightly higher accuracy than hybrid models implemented with IBM's quantum computers.

Ähnliche Arbeiten