Quantum Computing for Wildfire Prediction
Proof-of-concept algorithms executed on European quantum simulator (IBM Quantum / qPerfect MIMIQ)
Quantum Amplitude Estimation (QAE)
Quadratic speedup: O(1/ε) instead of O(1/ε²)
Same fire spread model, but quantum method estimates probability with fewer computational samples. Quantum estimate (0.166) matches analytical solution (0.168) with absolute error of 0.002.
Optimal Fire Station Placement (QAOA)
3/6 stations match classical optimal — 25 qubits, MIMIQ verified
QAOA algorithm finds near-optimal placements with 25 candidate locations. Optimized locally with Qiskit Aer, verified on MIMIQ cloud. QAOA cost: -16,800 vs optimal: -17,122 (~98% approximation ratio).
🌊 Iterative Quantum Amplitude Estimation
Iterative QAE uses Grover iterates to extract probability with higher precision. The characteristic sin²((2k+1)θ) oscillation proves real quantum amplitude estimation.
θ_fit
0.4146
θ_analytical
0.4224
P_estimated
0.162
Error
0.006
🚀 50-Qubit Quantum Simulation (MPS)
Large-scale simulation using MPS (Matrix Product State) backend for fire station placement optimization across all of Greece.
Qubits
50
Gates
3,875
Combinations
10.3B
Runtime
19m
50 candidate locations across Greece, selecting 10 optimal stations. Classical brute-force would require evaluating 10.27 billion combinations.
💨 Wind Impact on Fire Spread Probability
The quantum model generalizes across different wind scenarios.
Tailwind (NW)
46.3%
P(spread) = 0.85
Crosswind (NE)
12.5%
P(spread) = 0.65
Headwind (N)
1.8%
P(spread) = 0.4
Results computed on IBM Quantum / qPerfect MIMIQ, May 2026. Circuits: up to 50 qubits with MPS backend.
Fira Software Ltd, United Kingdom
Greek IKE in formation
Dr. Stamatis Kalogerakos, Director
MA Physics (Cambridge), MSc + EngD (Cranfield), PhD (Warwick)
Specialization: AI, Quantum Computing, Bayesian Estimation, Anomaly Detection, Algorithm Optimization