In the field of quantum machine learning, there are several key texts that are considered important for studying the intersection of quantum computing and machine learning. These texts provide foundational knowledge and insights into quantum algorithms, quantum information theory, and their applications to machine learning tasks. Here are a few notable books and research papers in this area:
"Quantum Machine Learning: What Quantum Computing Means to Data Mining" by Peter Wittek: This book provides a comprehensive introduction to quantum machine learning, covering topics such as quantum algorithms, quantum data representation, and quantum-enhanced learning models.
"Quantum Computing for Computer Scientists" by Noson S. Yanofsky and Mirco A. Mannucci: While not solely focused on machine learning, this book provides a solid foundation in quantum computing principles, algorithms, and complexity theory, which are essential for understanding the quantum aspects of machine learning.
"Quantum Algorithms for Supervised and Unsupervised Machine Learning" by Jacob Biamonte et al.: This research paper discusses quantum algorithms for machine learning tasks, including quantum support vector machines, quantum principal component analysis, and quantum clustering algorithms.
"Supervised learning with quantum computers" by Maria Schuld and Francesco Petruccione: This paper explores the applications of quantum computing to supervised learning tasks, discussing various quantum machine learning algorithms, including quantum neural networks and quantum kernel methods.
"Quantum-inspired machine learning: an introduction and overview" by Jacob Biamonte et al.: This survey paper provides an overview of quantum-inspired machine learning methods, which leverage classical machine learning techniques to simulate or approximate quantum algorithms.
"Machine learning with quantum states: recent progress and prospects" by Patrick Rebentrost et al.: This paper explores the use of quantum states and quantum algorithms for machine learning applications, including quantum feature spaces and quantum-inspired learning models.
These texts can serve as valuable resources for understanding the foundations and recent developments in the field of quantum machine learning. Additionally, it's important to keep in mind that the field is rapidly evolving, and new research papers and books are regularly published, expanding our understanding of quantum machine learning.