International Journal of Contemporary Research in Multidisciplinary

This review critically evaluates A Practical Guide to Quantum Machine Learning and Quantum Optimization by Elias F. Combarro and Samuel González-Castillo, a comprehensive and timely resource aimed at bridging the theoretical and practical dimensions of quantum computing. With a pragmatic focus on hands-on implementation, the book covers essential quantum algorithms and applications in optimization and machine learning, requiring only a foundational understanding of Python and linear algebra. The work is notable for its integration of rigorous theoretical exposition, executable code examples, and practical exercises, making it accessible to both academic and industry professionals. Despite some gaps in coverage, such as the omission of certain foundational algorithms and limited discussion of cloud-based platforms, the book excels in pedagogical clarity and practical relevance. This review highlights the strengths, limitations, and critical reception of the book, affirming its value as a key reference for computer science professionals engaged in quantum technologies.