Integrating Quantum Model Computing With Machine Learning: Quantum Algorithms For Accelerating Ai Model Training

Abstract

Quantum computing is a rapidly growing discipline that uses properties of quantum mechanics to compute problems that are challenging for traditional computational means. On the other hand, machine learning is a subset of artificial intelligence that enhances a computer's ability to learn patterns from experience. Massive data will get bigger with time; machine learning may sometimes be up to the task of big data, while quantum computing can perform fast computing. The crossbreed of quantum computing and machine learning brought about another field: quantum ML, or quantum machine learning. Applying the quantum learning algorithm includes benefiting from the fast processing in quantum computing and demonstrative speedup over the classical algorithm. Another subfield of artificial intelligence includes natural language processing, through which the computer can understand human languages. The chasers seek to apply quantum machine speedup in natural language processing. In this paper, firstly, we explain how from quantum computing to quantum machine learning. We then discuss the current trends regarding quantum machine learning for natural language processing. We also offer classic and quantum-based long short-term memory for POS tagging of code-mixed language and social media code mixed language. According to the above experiment, it is clear that the quantum-based long short-term memory works better than the classical long short-term memory for POS tagging for code-mixed datasets. Quantum computing is a rapidly developing field that uses the properties of quantum mechanics to solve problems challenging for classical computations.