Quantum Computing-Driven Optimization of Artificial Intelligence Algorithms and Cross-Domain Applications

Abstract

With the rapid development of artificial intelligence (AI) technology, traditional computing methods have encountered bottlenecks when processing large-scale data and complex models. Quantum computing, as an emerging computational paradigm, is expected to surpass classical computers in certain computational tasks by virtue of its unique properties such as quantum superposition and quantum entanglement. The introduction of quantum computing provides new perspectives and solutions for optimizing AI algorithms, particularly in enhancing computational speed, optimizing search strategies, and improving model training. This paper explores the applications of quantum computing in the field of AI, focusing on how quantum computing drives the optimization of AI algorithms through the integration of quantum machine learning (QML) and quantum optimization algorithms. Firstly, this paper reviews the fundamental principles of quantum computing and analyzes its applications in AI, including quantum support vector machines, quantum neural networks, and the Quantum Approximate Optimization Algorithm (QAOA). Next, this paper delves into the cross-industry applications of quantum computing in sectors such as healthcare, finance, and autonomous driving, and discusses how quantum computing can drive technological innovations in these fields. Despite its significant theoretical advantages, quantum computing still faces several technical challenges in practical applications, such as the stability of quantum hardware and the adaptability of quantum algorithms. This paper also explores these challenges and proposes future development trends for the integration of quantum computing and AI. Finally, this paper concludes that with the continuous advancement of quantum computing technology, quantum computing will play an increasingly important role in the field of AI, especially in large-scale data analysis, intelligent decision optimization, and solving complex problems. It is anticipated that AI algorithm optimization driven by quantum computing will not only advance academic research but also bring disruptive innovations to industrial applications.