Placement Optimization
With space traffic and debris increasing exponentially, efficient satellite placement is now mission critical. BQPhy’s QIEO algorithms optimize constellations faster than Genetic Algorithms, ensuring Maximum coverage with fewer satellites (reducing orbital congestion) and Safer orbital paths that minimize debris risk and low-traffic zones
Trajectory Optimization
Traditional trajectory planning struggles with real-time adjustments, scalability, and nonlinear dynamics—leaving satellite trajectory vulnerable to collisions or fuel waste. BQPhy’s QIEO solves Nonlinear orbital mechanics, perturbations, and non-convex problems faster than Genetic Algorithms.
Payload Optimization
Payloads are the heartbeat of every space mission - whether detecting wildfires with infrared sensors or capturing high-res earth imagery with Satellite SARs. But suboptimal payload allocation can derail mission success
Mission Planning & Scheduling
When it comes to space missions, every second for a satellite in orbit must deliver maximum value. But with thousands of tasks, conflicting objectives, and resource constraints, mission planning with classical algorithms might not be efficient