Hadamard Hooligans: Wildfire Mitigation w/ Quantum Annealing

Inspiration

We were inspired by the Tahoe Quantum Challenge and the real-world urgency of improving wildfire response. With climate change increasing the frequency and intensity of wildfires, we wanted to explore how quantum computing could help in making smarter, faster decisions under pressure.

What it does

Our program determines how to best allocate different wildfire suppression resources—like air support, ground crews, and controlled burns—across a grid-based map. It takes into account terrain types, fire spread, and resource effectiveness to minimize cost and maximize lives saved.

How we built it

We modeled the problem as a QUBO (Quadratic Unconstrained Binary Optimization) problem suitable for quantum annealing. By defining a custom Hamiltonian to represent the costs of suppression, benefits of lives spent, and other constraints, we were able to encode the wildfire system into a form that could be solved using quantum hardware.

Challenges we ran into

One major challenge was defining meaningful parameters—wildfires are highly dynamic and spatially complex. Capturing this behavior in a QUBO framework required balancing realism with computational feasibility. We also had to learn how to represent nonlinear factors (like future fire spread) in a binary optimization model.

Accomplishments that we're proud of

This was our first quantum computing project, and our first time working with quantum annealing. We're proud that we were able to take a complex real-world issue, model it mathematically, and complete a functioning quantum-enhanced program.

What we learned

We learned how to structure real-world problems for quantum optimization, including designing Hamiltonians, and understanding how constraints affect solution quality. We also learned a lot about quantum annealing