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Quantum-Enhanced Neural Networks for Cancer Detection Using Gene Expression Data

Suneel Kumar Komali, Persis Voola, Santhikumari Geddada

Published Oct 15, 2026• 0 citations
Publisher

Abstract

Accurate discrimination between benign and malignant tumours from molecular data is one of the most consequential tasks in clinical oncology, yet gene-expression profiles are extremely high-dimensional and challenge conventional classifiers with overfitting and heavy parameter budgets. This paper presents OncoVQC, a hybrid quantum-classical framework for cancer-malignancy classification from integrated gene-expression and clinical features. A disciplined classical pipeline of mutual-information feature selection, principal component analysis, and angle normalisation reduces fifty-eight raw features to six encoding angles, which are processed by a six-qubit Variational Quantum Classifier (VQC) employing genuine data re-uploading and a hardware-efficient entangling ansatz across four layers. The model is trained with the gradient-free COBYLA optimiser against a binary cross-entropy objective and is delivered behind a FastAPI prediction service with a React dashboard for real-time inference. On a balanced multi-cancer cohort of 5,200 samples spanning four tumour types, OncoVQC achieves 88.5% test accuracy and a ROC-AUC of 0.960 using only forty-eight trainable parameters two to four orders of magnitude fewer than competitive classical ensembles while an ablation study confirms that data re-uploading and entanglement are the decisive contributors to its performance.

DOI
10.5281/zenodo.23391310

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