ResQ: A Hybrid Classical-Quantum Model for Efficient Breast Cancer Image Classification

This is the official code release for the paper titled -

"ResQ: A Hybrid Classical-Quantum Model for Efficient Breast Cancer Image Classification"

Copyright 2025, Dibyasree Guha, Somenath Kuiry, Shyamali Mitra, Siddhartha Bhattacharyya and Nibaran Das, All rights reserved.

Abstract of the Paper

Breast cancer remains one of the leading causes of mortality worldwide, necessitating accurate and efficient diagnostic systems to improve treatment outcomes. While deep learning-based computer-aided diagnostic (CAD) tools have demonstrated promise in analyzing histopathological images, they face challenges in handling high-dimensional data and computational inefficiencies. Simultaneously, quantum computing has emerged as a transformative technology, offering unparalleled capabilities in modeling complex data distributions and accelerating computations. This paper introduces ResQ, a hybrid classical-quantum framework designed for breast cancer classification. ResQ integrates a ResNet-based feature extraction module with a Variational Quantum Circuit (VQC) classifier, leveraging the complementary strengths of classical deep learning and quantum computing. Evaluations on two publicly available datasets, BreakHis and Bioimaging, reveal significant performance improvements, achieving accuracies of $98.49%$ and $88.96%$, respectively, compared to $97.36%$ and $87.96%$ achieved by its classical counterparts. \textcolor{blue}{Quantum circuit evaluations have been conducted on quantum simulator as well as noisy real quantum processor.} Additionally, to gain deeper insights into the effectiveness of the ResQ model over classical models, a non-parametric statistical test, namely, the Friedman Test, followed by Nemenyi Test for post hoc analysis, are performed. Furthermore, a detailed circuit-level analysis explores critical trade-offs, such as circuit depth, gate count, and qubit usage, providing unique insights into the practical deployment of hybrid classical-quantum models in medical imaging. \textcolor{blue}{The one-qubit shallow architecture of ResQ renders lower circuit complexities making it amenable to Noisy-Intermediate Scale Quantum (NISQ) devices.} These findings underscore the potential of quantum computing to revolutionize cancer diagnostics by enhancing both accuracy and computational efficiency.

Dataset Folder Organization

Dataset_Name
   |-- Original
       |-- Train
           |-- Benign
           |-- Malignant
       |-- Test
           |-- Benign
           |-- Malignant
       |-- Validation
           |-- Benign
           |-- Malignant

External Packages Required

• Numpy 2.0.2
• PIL 5.0.0
• Scipy 1.15.2
• Matplotlib 3.8.4
• Pytorch 0.4.0
• PennyLane 0.41.1
• qiskit 2.0.0
• qiskit-ibm-runtime 0.37.0