Quantum-Inspired Multi-Phase Missile Trajectories

Anti-ship missiles play a decisive role in modern naval warfare. The terminal trajectory phase is particularly critical, requiring precise control over speed, altitude, and attitude within seconds. Traditional optimization methods, while advanced, still face limits in handling such real-time complexities.

As defense systems demand greater adaptability and precision, optimization frameworks must evolve beyond classical approaches.

Why Trajectory Optimization is Challenging

Unlike ballistic systems that follow predictable paths, anti-ship missiles must continuously adapt to countermeasures and evasive maneuvers. This creates several challenges:

• High Dynamics and Constraints – altitude, thrust, and overload limits within narrow margins
• Real-Time Data Dependence – radar seekers and tracking systems demand instant updates
• Multiphase Complexity – cruise, altitude adjustment, and terminal dive require distinct optimization
• Nonlinear Coupling – aerodynamic interactions make analytical solutions impractical
• Computational Burden – dimensionality grows exponentially with time steps and constraints

Limitations of Classical Optimization

Classical optimization methods fall short in dynamic missile guidance.

• Indirect approaches based on Pontryagin’s Maximum Principle are highly sensitive to initial guesses and perform poorly with nonlinear path constraints.
• Direct approaches such as GA, PSO, and ACO are more flexible but suffer from premature convergence and poor scalability.
• Hybrid methods show improvements, reducing miss distance and achieving favorable terminal angles. However, their high computational cost limits real-time feasibility.

How Quantum-Inspired Optimization Helps

Quantum-inspired optimization (QIO) introduces a practical alternative to overcome these limitations.

• Exploration of Search Spaces – maintains diversity, avoiding premature convergence
• Real-Time Adaptability – integrates live updates from seekers into the guidance loop
• Constraint Handling – manages altitude, overload, and velocity simultaneously
• Scalability – efficiently addresses high-dimensional trajectory problems
• Operational Relevance – supports re-optimization mid-flight against evasive targets

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