In this work we present an empirical-based comparative analysis of quantum circuit simulators that are implemented using C and Python programming languages, focusing on advanced measurement capabilities with state collapse mechanisms. The simulators support full quantum state evolution and feature our unique contribution that allows both projective (collapsing) and weak (non-collapsing) measurements. Our implementation extends traditional quantum simulation by incorporating flexible measurement schemes that can maintain quantum superposition when desired, enabling the study of quantum trajectories and partial measurements. The C implementation focuses on performance through optimized memory management and efficient tensor contractions. The Python version, while slower, offers better extensibility and seamless integration with existing scientific computing libraries and insightful contributions from the vast community of the language developers. This will facilitate fast prototyping and analysis thereby making the study of quantum simulation more accessible to researchers in the field. Both versions implement identical quantum operations, allowing validation of results across implementations. Our implementation achieves simulation capabilities of up to 29 qubits in C and 20 qubits in Python, with demonstrated zero back-action across weak measurements (0.1–0.5 strength). The direct method shows approximately 30% better performance than the tensor method for larger systems, while maintaining measurement precision with outcome distributions centered at 0.5. Validation through GHZ state preparation demonstrates perfect qubit correlations, confirming reliable quantum state manipulation. Performance benchmarks demonstrate the trade-offs between computational efficiency and development flexibility, providing insights for choosing appropriate tools for different quantum simulation scenarios.