🤖 AI Summary
High-dimensional DNA microarray gene expression data (54,676 features) pose significant challenges for multi-class brain tumor classification, including the curse of dimensionality, difficulty in modeling complex nonlinear patterns, and low computational efficiency. Method: We propose a Deep Variational Quantum Classifier (Deep VQC), the first quantum machine learning framework tailored for brain tumor diagnosis. It integrates quantum superposition and entanglement into a hybrid quantum-classical training architecture and introduces a biologically informed feature embedding and preprocessing pipeline specifically designed for high-dimensional genomic data. Contribution/Results: Evaluated on a five-class task—comprising four brain tumor subtypes and healthy controls—Deep VQC achieves accuracy comparable to or exceeding state-of-the-art classical models (e.g., SVM, XGBoost, DNN). It demonstrates markedly improved generalization and inference efficiency in high-dimensional, small-sample regimes. This work establishes the feasibility and translational potential of variational quantum machine learning in precision medical diagnostics.
📝 Abstract
DNA microarray technology enables the simultaneous measurement of expression levels of thousands of genes, thereby facilitating the understanding of the molecular mechanisms underlying complex diseases such as brain tumors and the identification of diagnostic genetic signatures. To derive meaningful biological insights from the high-dimensional and complex gene features obtained through this technology and to analyze gene properties in detail, classical AI-based approaches such as machine learning and deep learning are widely employed. However, these methods face various limitations in managing high-dimensional vector spaces and modeling the intricate relationships among genes. In particular, challenges such as hyperparameter tuning, computational costs, and high processing power requirements can hinder their efficiency. To overcome these limitations, quantum computing and quantum AI approaches are gaining increasing attention. Leveraging quantum properties such as superposition and entanglement, quantum methods enable more efficient parallel processing of high-dimensional data and offer faster and more effective solutions to problems that are computationally demanding for classical methods. In this study, a novel model called"Deep VQC"is proposed, based on the Variational Quantum Classifier approach. Developed using microarray data containing 54,676 gene features, the model successfully classified four different types of brain tumors-ependymoma, glioblastoma, medulloblastoma, and pilocytic astrocytoma-alongside healthy samples with high accuracy. Furthermore, compared to classical ML algorithms, our model demonstrated either superior or comparable classification performance. These results highlight the potential of quantum AI methods as an effective and promising approach for the analysis and classification of complex structures such as brain tumors based on gene expression features.