Hybrid Quantum-Classical Graph Neural Networks for Tumor Classification in Digital Pathology

Advances in classical machine learning and single-cell technologies have paved the way for understanding interactions between disease cells and tumor microenvironments towards accelerating therapeutic discovery. However, challenges in these machine learning methods and NP-hard problems in spatial biology create an opportunity to explore quantum computing algorithms. Here, we present a hybrid quantum-classical graph neural network (GNN) that combines a classical GNN with a type of quantum neural network (QNN) called a variational quantum classifier (VQC) for classifying binary sub-tasks in breast cancer subtyping. We explore two variants of the framework, the first with fixed pretrained GNN parameters and the second with end-to-end training of GNN+VQC. The results demonstrate that the hybrid quantum-classical neural network is at par with the state-of-the-art classical graph neural networks in terms of weighted precision, recall, and F1-score at higher dimensions. We also show that by means of amplitude encoding, we can compress information in logarithmic number of qubits and attain better performance in comparison to classical compression (which leads to information loss), while keeping the number of qubits required constant in both regimes. Finally, we show some results that end-to-end training enables an improvement over fixed GNN parameters and also slightly improves over vanilla GNN with the same number of dimensions.