Abstract
The article tackled the challenge of real-time data processing in dynamic, large-scale systems by examining machine learning strategies enhanced through quantum computing. As sectors such as smart cities and autonomous vehicles generated high-velocity, complex data, conventional algorithms faced limitations in adaptability and efficiency. This study systematically reviewed advanced quantum methodologies including quantum support vector machines, quantum particle swarm optimization, and quantum-enhanced gradient boosting and highlighted performance improvements over classical models. By leveraging specialized hardware and frameworks like GPUs and federated learning, these quantum-enhanced models achieved superior accuracy, scalability, and computational efficiency. Key stages in the proposed framework, such as data preprocessing and model adaptation, maximized efficiency, enabling real-time adaptability in dynamically changing environments. Findings demonstrated that integrating quantum techniques addressed significant computational challenges, showing strong potential for applications requiring rapid data response, minimal latency, and scalability. This research underscores the transformative impact of quantum-enhanced machine learning, offering robust solutions that could reshape complex, data-intensive industries dependent on real-time processing, such as healthcare, transportation, and finance. Future advancements in quantum computing could further solidify machine learning’s role in managing the demands of an increasingly data-driven world.