Quantum Drone Path Optimizer

Inspiration

Today, drones are being employed for a variety of applications, from delivering packages to surveillance. However, most pathfinding algorithms struggle to adapt when subjected to dynamic, multi-dimensional terrains and no-fly zones. I thought that using quantum algorithms would be purfect for dealing with the scalability and complexity of this problem.

What it does

The Quantum Drone Path Optimizer leverages Quantum Approximate Optimization Algorithms (QAOA) to find the most efficient path for drones in a 3D environment. The algorithm takes into account waypoints and no-fly zones to produce an optimized path that minimizes both distance and time.

How we built it

The project was implemented using Python, integrating libraries like NumPy for mathematical operations and Qiskit for quantum computing functionality. We designed a 3D grid system to represent the environment, including waypoints and no-fly zones, and utilized QAOA to find the optimal path through this grid.

Challenges we ran into

1. The algorithm currently crashes when applied to a 10x10x10 grid.
2. Integration of classical and quantum computing algorithms was initially challenging.
3. Takes a while to set up the quadratic program in the initial stages.
4. Encountered issues when setting up the quadratic program suitable for the quantum eigen solver.

Accomplishments that we're proud of

1. Theoretical application of QAOA in a drone path optimization scenario.
2. Framework setup for handling environments with multiple waypoints and no-fly zones, even if not fully functional yet.
3. Deepening our understanding of the integration challenges and complexities between classical and quantum computing paradigms.

What we learned

1. Deepened understanding of Quantum Computing and QAOA.
2. Insights into the complexities of 3D pathfinding algorithms.
3. Importance of computational efficiency and optimization in real-world applications.
4. Challenges in setting up quadratic programs suitable for quantum eigen solvers.

What's next for Quantum Drone Path Optimizer

1. Implementing real-time updates to the drone's path based on dynamic changes in the environment.
2. Working to make the algorithm scalable for larger grid sizes and more complex scenarios.
3. Partnering with drone manufacturers and service providers for potential integration once fully functional.

Built With

• ibm-cloud
• numpy
• python
• qiskit