Hybrid classical and quantum computing for enhanced glioma tumor classification using TCGA data.

ABSTRACT: Gliomas are the most prevalent malignant primary brain tumors and present diagnostic challenges due to varying survival rates and treatment responses between low-grade gliomas (LGGs) and high-grade gliomas (HGGs). Accurate classification is crucial for effective treatment and prognosis. While classical AI methods have shown promise in glioma classification, the growing volume of medical data, inherent noise, and limitations of classical vector spaces present significant challenges. However, quantum computing-based AI methods have the potential to process data in parallel by leveraging quantum properties such as superposition and entanglement, analyze higher-dimensional data more efficiently, and solve certain problems that classical methods struggle with more rapidly and effectively. This

SUBMITTER: Akpinar E 

PROVIDER: S-EPMC12271389 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature