QUANTUM ALGORITHMS FOR OPTIMIZING LARGE-SCALE DATA PROCESSING TASKS IN CLASSICAL

Abstract

The research examines how quantum algorithms can enhance large data processing capabilities of existing computer systems. The growing complexity of data and processing needs makes traditional systems insufficient in optimizing, sorting and searching operations. This work focuses its main effort on developing hybrid quantum-classical algorithms which utilize quantum parallelism capabilities while maintaining classical system compatibility. The research explores essential challenges linked to security flaws and scalability issues as well as data processing work execution problems. The testing of proposed quantum algorithms involved security, scalability and functionality assessment decisions as the fundamental evaluation criteria. Security testing under these enhanced protective measures reached complete success by removing major flaws from reentrancy breaches and integer overflow which led to 100% protection. The system demonstrated good functionality during network congestion tests involving moderate loads yet performance degradation occurred when congestion was severe so further optimizations will make it suitable for widespread usage. The functional testing results demonstrated the system accomplished 100% token transfer success rate together with 97.5% asset management success rate and 96% success rate for its decentralised exchange operations. General implementation requires fixing scalability and distributed exchange optimisation issues however current study results indicate quantum algorithms show strong potential for data processing optimization tasks. The research provides valuable insights about hybrid quantum-classical systems by presenting a road to safe, effective and scalable processing for big applications.