# Histopathological Cancer Detection Using Hybrid Quantum Computing

arXiv: https://arxiv.org/abs/2302.04633 · HTML: https://mlsanity.com/paper/2302.04633/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Feb 2023.

## Built on by (most cited first, 4 of 4)

- [Towards Transfer Learning for Large-Scale Image Classification Using Annealing-based Quantum Boltzmann Machines](https://mlsanity.com/paper/2311.15966/) (2023)
- [Configurable Algorithms for Histopathologic Cancer Detection on Quantum Hardware](https://mlsanity.com/paper/2606.21752/) (2026)
- [Quantum Computing Supported Adversarial Attack-Resilient Autonomous Vehicle Perception Module for Traffic Sign Classification](https://mlsanity.com/paper/2504.12644/) (2025)
- [Stochastic Entanglement Configuration for Constructive Entanglement Topologies in Quantum Machine Learning with Application to Cardiac MRI](https://mlsanity.com/paper/2507.11401/) (2025)

## Abstract (arXiv)

We present an effective application of quantum machine learning in the field of healthcare. The study here emphasizes on a classification problem of a histopathological cancer detection using quantum transfer learning. Rather than using single transfer learning model, the work model presented here consists of multiple transfer learning models especially ResNet18, VGG-16, Inception-v3, AlexNet and several variational quantum circuits (VQC) with high expressibility. As a result, we provide a comparative analysis of the models and the best performing transfer learning model with the prediction AUC of approximately 93 percent for histopathological cancer detection. We also observed that for 1000 images with Resnet18, Hybrid Quantum and Classical (HQC) provided a slightly better accuracy of 88.5 percent than classical of 88.0 percent.