6. Quantum-Inspired Path and Task Optimization

The Optimization and Pathfinding Engine (OPE) integrates quantum heuristic search protocols for near-optimal path discovery and task allocation in combinatorially complex environments.

6.1 Grover Search-Driven Pathfinding

A simulated Grover’s search algorithm accelerates the identification of optimal navigational paths within grid-based terrains.

• State Vector Initialization: Represents operational cells as quantum states (0=open, 1=obstacle).

• Oracle Function Design: Flags valid goal states within superposition.

• Amplitude Amplification: Iteratively enhances probability amplitudes of optimal solutions.

• Measurement Collapse: Final state selection via probabilistic measurement.

6.2 Genetic Algorithm Hybridization

Task assignment optimizations leverage a hybrid approach combining Grover-enhanced search with classical genetic algorithms (GAs).

• Chromosome Encoding: Task allocations encoded as ordered gene sequences.

• Fitness Function: Evaluates task schedules by minimizing total operation time and resource contention.

• Crossover & Mutation Operators: Ensures population diversity and convergence towards global optima.

• Quantum-Guided Mutation: Dynamically adjusts mutation probabilities based on Grover-derived heuristics.