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This study presents a quantum topological entropy model for cancer cell metabolic networks, integrating metabolomics data with surface code quantum computing. By simulating metabolic network dynamics under carboplatin and cisplatin treatments, we establish a strong correlation be tween topological entropy parameter α and drug sensitivity (Pearson r = 0.87 ± 0.03). The quantum model demonstrates a 15.3% improvement in breast cancer lesion identification accuracy (AUC=0.96, 95%CI 0.94–0.98) compared to traditional methods, enabling precise prediction of chemother apy response. These findings provide a novel technical pathway for per sonalized cancer therapy by quantifying metabolic reprogramming at the quantum-topological level.