## Inspiration

Wanted to do something with quantum computing and was interested in quantum hybrid algorithms

## What it does

Applies the traveling salesman problem to the airplane industry and uses a quantum computer to compute the optimal solution

## How we built it

- I took a list of airports, found their latitude/longitude to create a distance matrix
- Created a graph and mapped the distance matrix to the graph
- Mapped it to the Ising model then sent it to the quantum algorithm to optimize
- Take the answer and determined which path to take

## Challenges we ran into

- Figuring out what the functions do and what the variables do in the Qiskit tutorial
- Trying to read and understand the Qiskit documentation
- Constant trial and error

## Accomplishments that we're proud of

- Getting the distance matrix from the airports mapped onto the real graph for the quantum algorithm
- Getting the code to work

## What we learned

- How the math/theory works for the algorithm
- How to create a distance matrix from scratch

## What's next for Airplane Optimizer

- Create parameters and airspace limits (ie. can't fly over White House, etc.)
- Have it work for more airports (more nodes)

## Built With

- python
- qiskit

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