  This paper tackles the multi-vehicle Coverage Path Planning (CPP) problem,
crucial for applications like search and rescue or environmental monitoring.
Due to its NP-hard nature, finding optimal solutions becomes infeasible with
larger problem sizes. This motivates the development of heuristic approaches
that enhance efficiency even marginally. We propose a novel approach for
exploring paths in a 2D grid, specifically designed for easy integration with
the Quantum Alternating Operator Ansatz (QAOA), a powerful quantum heuristic.
Our contribution includes: 1) An objective function tailored to solve the
multi-vehicle CPP using QAOA. 2) Theoretical proofs guaranteeing the validity
of the proposed approach. 3) Efficient construction of QAOA operators for
practical implementation. 4) Resource estimation to assess the feasibility of
QAOA execution. 5) Performance comparison against established algorithms like
the Depth First Search. This work paves the way for leveraging quantum
computing in optimizing multi-vehicle path planning, potentially leading to
real-world advancements in various applications.
