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.
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