How Programming Languages Enhance Mathematical Modelling with AI and Quantum Computing

What is it about?

The study investigates the impact of programming languages on mathematical modelling, highlighting technologies such as cloud computing, AI, and hybrid algorithms that enhance model precision and effectiveness. It compares Python, C++, and Julia, noting Python's rapid development capabilities, C++'s performance for computationally demanding tasks, and Julia's balance of usability and performance. The integration of quantum computing and machine learning is also noted for addressing complex problems. The research underscores the growing importance of combining traditional methods with modern languages and technologies to improve mathematical modelling. The study involves a comprehensive narrative review, examining various scholarly sources to explore the evolution and integration of mathematical modelling with contemporary computing advancements.

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Why is it important?

This research is important because it delves into the integration of programming languages with mathematical modelling, which is crucial for solving complex scientific and technical problems in an era of rapidly advancing technology. By exploring the role of languages like Python, C++, and Julia, and integrating cutting-edge technologies such as cloud computing, AI, and quantum computing, the study highlights the potential for increased accuracy and efficiency in mathematical models. This is particularly significant as it opens new avenues for addressing challenging issues in various scientific domains, thereby enhancing the capability to model complex systems and processes effectively. Key Takeaways: 1. Language Performance: The study compares the utility of Python, C++, and Julia in mathematical modelling, with Python favoring rapid development via its extensive libraries, C++ excelling in performance for demanding tasks, and Julia offering a middle ground between usability and performance. 2. Technological Integration: The research emphasizes the enhancement of mathematical models through advanced technologies like cloud computing, AI, and hybrid algorithms, which significantly improve model precision and efficiency. 3. Quantum Computing Potential: The study highlights quantum computing's promise in revolutionizing dynamic and large-scale modelling, enabling the handling of complex optimization problems much faster than traditional methods, with potential applications in fields like drug discovery and climate modelling.