Potential in Quantum Machine Learning for Real-World Problems

Quantum computing offers exponential speedup over classical computing, particularly beneficial for machine learning tasks burdened by large datasets and complex models. Quantum machine learning (QML) exploits quantum principles like superposition and entanglement to enhance efficiency and scalability. QSVMs and quantum k-means algorithms exemplify QML's prowess in classification and clustering tasks, respectively. QML's potential extends to solving real-world problems in diverse domains, including chemistry, logistics, and healthcare. However, hardware limitations and the interdisciplinary nature of QML present challenges. Nonetheless, QML holds promise for revolutionizing computational solutions across industries, offering faster, more efficient, and scalable approaches. As research progresses in quantum computing and machine learning, the impact of QML on society and industry is expected to increase, heralding a new era of computational innovation.