  In quantum computing, quantum compilation involves transforming information
from 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, such as simulating systems with varying parameters and
preparing multi-component quantum states. To address this, we develop a
multi-target quantum compilation algorithm to enhance the performance and
flexibility of simulating multiple quantum systems. Through our benchmarks and
case studies, we demonstrate the algorithm's effectiveness, highlighting the
significance of multi-target optimization in the advancement of quantum
computing. This work establishes the groundwork for further development,
implementation, and evaluation of multi-target quantum compilation algorithms.
