This project seeks to combine quantum algorithms and classical reinforcement learning to guide a swarm of autonomous agents through a two-dimensional environment. The agents are initialized at an arbitrary location, must avoid obstacles while searching for a target, and may obey leader-follower dynamics. The integration of quantum algorithms must operate efficiently on modern quantum computers through IBM Platform. Our team analyzes solutions using Grover’s algorithm and variational quantum algorithms to establish the best framework to proceed with. Variational quantum circuits are shown to have greater efficiency and wider applicability. Our team adapts existing methods to better suit the engineering problem at hand through the use of Python.