Quantum-Inspired Hybrid Machine Learning Framework for Scalable Computational Next-Generation Technologies

This paper examines quantum computing’s revolutionary potential to overcome classical computing’s limitations and unleash unprecedented computational power to promote innovation across many industries. Quantum computing might revolutionize AI, cryptography, simulations, and finance by solving scalability, optimization, and real-time processing issues. Traditional computer approaches struggle with complicated difficulties, especially in machine learning (ML), which demands more efficient processing due to enormous datasets and numerous sophisticated models. We introduced Quantum-Inspired Hybrid Machine Learning Models (Q-IHMLM) that combine traditional and quantum techniques. This paradigm aims to bridge this gap. Q-IHMLM uses quantum mechanics to boost computational efficiency, which speeds up data processing, improves forecasts, and improves financial modeling for portfolio optimization, risk assessment, and fraud detection. The suggested technique is adaptable since it is employed in big data analytics, AI model training, optimization, and financial analysis. The abstract should be refined to explicitly highlight the quantum circuit structure used, detailing gate composition and qubit connectivity. It must also describe the encoding mechanisms that map classical data into quantum states. Additionally, the role of algorithmic differentiation in training and optimization should be clarified. These elements will provide a more complete technical foundation for the proposed work. Q-IHMLM speeds up problem-solving while maintaining accuracy and saving processing resources, making real-world applications more efficient and scalable. We found that Q-IHMLM outperforms other approaches in real-time data processing, accuracy, and scalability. This achievement affects the banking industry and other organizations and sets the way for computer technology improvements.