# Network separation modeling and quantum computing for developing wildfire fuelbreak strategy

## TL;DR

This paper introduces a new method using network science and quantum computing to optimize the placement of wildfire fuelbreaks.

## Contribution

The novel use of equal graph partitioning and D-Wave’s quantum computing for efficient fuelbreak placement is presented.

## Key findings

- Quantum computing reduced fuelbreak placement time to seconds for a given area.
- Two placement alternatives showed improvements in land separation equality compared to traditional methods.
- D-Wave’s solver outperformed SCIP but was slower than CPLEX in a subsection test.

## Abstract

Fuelbreak placement is an important consideration in fire management. Historically, strategies for placing fuelbreaks have fallen on the experience of fire managers such as by following ridgelines, and recent searches for a formal placement strategy have struggled to scale to large areas. Here we present a basic strategy utilizing equal graph partitioning and quantum computing to efficiently determine placements. By posing partitioning as a quadratic constrained binary optimization problem, D-Wave’s hybrid quantum optimization tool could complete the task in seconds. Results for the examined area show two alternatives to the ridgeline method in a so-called worst-case fire scenario: one with 2.9% improvement in land separation equality while clearing 76 less acres, and another with a 12.4% improvement by clearing 19 more acres. In a selected subsection, D-Wave’s hybrid solver performed faster than the SCIP solver but slower than the CPLEX solver, with the prospect for increased speed-up on larger problems. These findings demonstrate the effectiveness of equal graph partitioning for fuelbreak placement and the potential of D-Wave’s hybrid solvers.

Fuelbreaks are treated areas of land that mitigate the spread of wildfires. Dent and colleagues present a method for determining optimal placement using network science and partitioning through optimization with the D-Wave hybrid quantum solver.