UNIFIED QUANTUM-CLASSICAL HYBRID ALGORITHMS FOR SOLVING NP-HARD PROBLEMS IN LARGE-SCALE DISTRIBUTED COMPUTING ENVIRONMENTS: A RESOURCE-AWARE CO-EXECUTION FRAMEWORK INTEGRATING QPU ACCELERATION, HEURISTIC OPTIMIZATION, AND SCALABLE TASK OFFLOADING

This paper explores a novel hybrid approach that combines classical and quantum computing techniques to solve NP-hard problems in large-scale distributed environments. We propose a resource-aware co-execution framework that integrates Quantum Processing Unit (QPU) acceleration with heuristic optimization and scalable task offloading mechanisms. The proposed framework aims to optimize task execution by dynamically leveraging quantum and classical resources in a complementary manner. The effectiveness of this hybrid approach is evaluated through multiple benchmarks, showcasing significant improvements in computational efficiency and scalability. This research opens new possibilities for applying quantum computing in solving real-world computationally intensive problems.