# QUAV: Quantum-Assisted Path Planning and Optimization for UAV Navigation with Obstacle Avoidance

## TL;DR

QUAV leverages quantum algorithms to enhance real-time UAV path planning, effectively managing obstacle avoidance and scalability challenges in complex environments.

## Contribution

This paper introduces QUAV, a novel quantum-assisted framework for UAV path planning that applies QAOA to optimize trajectories with obstacle constraints.

## Key findings

- QUAV achieves linear circuit depth scaling with path complexity.
- Simulations and hardware tests validate QUAV's robustness and efficiency.
- Quantum approach shows promise for scalable drone navigation.

## Abstract

The growing demand for drone navigation in urban and restricted airspaces requires real-time path planning that is both safe and scalable. Classical methods often struggle with the computational load of high-dimensional optimization under dynamic constraints like obstacle avoidance and no-fly zones. This work introduces QUAV, a quantum-assisted UAV path planning framework based on the Quantum Approximate Optimization Algorithm (QAOA), to the best of our knowledge, this is one of the first applications of QAOA for drone trajectory optimization. QUAV models pathfinding as a quantum optimization problem, allowing efficient exploration of multiple paths while incorporating obstacle constraints and geospatial accuracy through UTM coordinate transformation. A theoretical analysis shows that QUAV achieves linear scaling in circuit depth relative to the number of edges, under fixed optimization settings. Extensive simulations and a real-hardware implementation on IBM's ibm_kyiv backend validate its performance and robustness under noise. Despite hardware constraints, results demonstrate that QUAV generates feasible, efficient trajectories, highlighting the promise of quantum approaches for future drone navigation systems.