This study explores the quantitative correlation between Calabi-Yau (CY) manifolds and nuclear topological properties through an exploratory simulation framework based on quantum computing. The computational system comprises five core modules: a geometric parameterization model for CY manifolds, a Hodge topology quantization algorithm, a supersymmetric constraint adaptation module, a quantum Chern class mapping operator, and a nuclear physical quantity decoding module, enabling quantitative mapping from high-dimensional manifold topological features to key nuclear parameters (proton number, neutron number, binding energy). Twelve feature transformation formulas adapted for quantum computing were derived to optimize the matching accuracy between manifold topological invariants (Hodge numbers, Chern classes) and nuclear physical parameters. Simulation validation using 100 typical nuclear samples demonstrated that the average deviation between quantum computing outputs and experimental measurements remained below 0.002%, preliminarily indicating the framework's reliability and accuracy in cross-disciplinary correlation simulations. This research provides a quantumized research approach for the intersection of string theory and nuclear physics, revealing potential profound connections between microscopic nucleon structures and high-dimensional manifold topologies.