Current quantum processors must be scaled to match the computational power needed for quantum algorithms that present quantum advantage. It is not feasible to keep integrating more qubits on a single core because qubit crosstalk and decoherence make the computations too noisy. Multi-core approaches have been proposed as a feasible, scalable alternative. Optimizing the mapping of quantum algorithms onto multi-core quantum computers is crucial for improving performance and scalability in quantum computing systems. This thesis presents a pipeline for computing the complete mapping of quantum algorithms into multi-core quantum computers, considering both intra-core and inter-core constraints. The existing state-of-the-art inter-core mapping algorithms are evaluated and found to be suboptimal for achieving near-optimal inter-core communications. An alternative inter-core mapping algorithm is proposed to address this limitation, demonstrating improved effectiveness in allocating quantum operations across multiple cores. A thorough comparison of the proposed algorithm and existing approaches is conducted using the complete mapping pipeline. The findings emphasize the importance of developing efficient inter-core mapping algorithms. The research outcomes contribute to advancing the field of quantum computing and provide valuable insights for optimizing performance and scalability in multi-core quantum computers.