In quantum computing, quantum compilation transforms a target unitary into a trainable unitary represented by a quantum circuit. Traditional quantum compilation optimizes circuits for a single target. However, many quantum systems require simultaneous optimization of multiple targets. To address this, we developed a multi-target quantum compilation algorithm to enhance the performance and flexibility of simulating multiple quantum systems. Our benchmarks and case studies demonstrate the algorithm’s effectiveness, highlighting the significance of multi-target optimization in advancing quantum computing. This work establishes the foundation for further development and evaluation of multi-target quantum compilation algorithms.