Quantum‐Inspired Optimization Techniques for IoT‐Driven Big Data Analysis

Recently, many optimization techniques have been proposed by researchers to handle the information generated by the Internet of Things (IoT) environment, and this information is of high volume, heterogeneous, and complex in nature. The traditional optimization techniques are not support for achieving high accuracy due the issues present in the high-dimensional, dynamic, and nonlinear nature of IoT-driven big data. This chapter provides are detailed view about the use of quantum-inspired optimization techniques for enhancing the efficiency of optimization techniques for improving the efficient in problem solving. The chapter presents an overview of the IoT environment with the application of big data techniques to handle complex problems. The fundamental concepts of quantum principles were explored using the key concepts of superposition, entanglement, and quantum tunnelling. This chapter provides a detailed discussion of the various types of quantum-inspired optimization techniques, including Quantum Annealing, Quantum-Inspired Genetic Algorithms, and Quantum-Inspired Particle Swarm Optimization. This discussion provides a detailed review of the quantum mechanisms, challenges, advantages, and limitations. The performance evaluation of the quantum-inspired optimization techniques is compared with traditional techniques for measuring the scalability, accuracy, time complexity, and convergence rate. This chapter addresses the challenges of using a quantum-inspired technique, including computational cost, design complexity, and application customization. This chapter concludes with a detailed view of emerging trends and future research directions for young researchers. Future directions may cover design issues in combining quantum concepts with machine learning approaches for IoT big data analytics. This chapter will be a comprehensive guide for academicians, researchers, and business people, and it offers innovative approaches for binding the principles of quantum-inspired optimization mechanisms for transforming IoT-driven big data analysis.