Dr. Israa Abdulrauof, Assistant professor and researcher with 7+ years of experience teaching courses in both undergraduate and postgraduate levels. Supervised BA, MA, and Ph.D. theses. Dissertations. Edited and Co-authored 2 monographs on contemporary trends in political thought. Published 7 articles in peer-reviewed journals. One published book. Founded an educational channel on YouTube, I am a Consultants Network Member of IEEE ,Young Professional member and sight member of IEEE - the world’s largest professional association for the advancement of technology
Data Science, Machine Learning, Artificial Intelligence, Software Engineering
Quantum Machine Learning: The Revolutionary Combination of Quantum Computing and Artificial Intelligence
Quantum computing powered by artificial intelligence (AI) to tackle advanced challenges in data processing and optimization as well as decision-making. For example, this paper is about the intersection of quantum computing, specifically what we can get with it that using traditional machine learning models does not suffice to solve tasks which classical computer cannot approximately or efficiently tackle.
Quantum machine learning techniques can radically change the future of AI by providing faster, improved computational abilities for pattern recognition, data classification and anomaly detection. We review the basic ideas from quantum information theory, and how these apply to tasks in machine learning (especially: QNNs and quantum-enhanced optimization algorithms). It also provides an overview of domain-specific applications of quantum machine learning (QML) in finance, health care, and cryptography such as quantum data classification, clustering, and regression.
Furthermore, we begin with a review of the limitations of present-day quantum hardware, then explore future milestones that could lead to functional quantum machine learning systems. This is an exploration of the transformative potential of quantum machine learning, as we conduct a survey on properties and performance of AI at the edge, identifying not only its advantages but also how it takes us beyond classical computing in solving problems that remain intractable for standard machines