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Optimization Solver
Optimization Solver

Launch Vehicle Trajectory Optimization Powered by Quantum-Inspired Evolutionary Optimization (QIEO) algorithms

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Challenges

Designing optimal launch vehicle trajectories across multiple mission phases—ascent, hover, and descent—under real-world constraints:

  • Optimize throttle profiles to minimize fuel usage
  • Maintain stable hover altitude within tight tolerances
  • Navigate nonlinear dynamics like drag, gravity, and mass reduction
  • Avoid convergence issues seen in classical solvers (e.g., Genetic Algorithms)

Results

QIEO delivers faster convergence, stable performance, and lower fuel usage:

Metric QIEO Classical GA
Feasible Solutions (10 runs) 10/10 valid 6/10 failed constraints
Fuel Consumption ~22.6 kg (consistent) Highly variable
Convergence Time ~15× faster Slower at high scale
Scalability Robust across high time-steps Degraded with problem size

QIEO delivers faster convergence, stable performance, and lower fuel usage:The optimizer generated an adaptive throttle profile across all mission phases:

  • Ascent (0–7.5s): Smooth acceleration with high thrust
  • Hover (7.5–12.5s): Stable altitude maintained with minimal throttle
  • Descent (12.5–17.5s): Reduced thrust enabling controlled descent
  • Thrust: 7000 N
  • Hover Constraint: Maintain steady altitude
  • Fuel Used: 22.62 kg

Thrust varied intelligently across the timeline, and the altitude remained stable during hover—demonstrating precise constraint handling and dynamic control.

QIEO improves trajectory optimization results

15× faster convergence

Accelerated solution time compared to classical Genetic Algorithms

Fuel-efficient control profiles
Reduced propellant use while satisfying all mission constraints
Stable performance at high complexity
Handled nonlinear dynamics and multi-phase constraints reliably at scale
Go Beyond Classical Limits.
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