A Practical Guide to Quantum Machine Learning and Quantum Optimization 1st Edition

A Practical Guide to Quantum Machine Learning and Quantum Optimization (1st Edition) by Elías F. Combarro, Samuel González-Castillo, and Alberto Di Meglio delivers a pragmatic, expertly guided entry into the rapidly evolving intersection of quantum computing and machine learning. This book grabs attention with clear explanations of core concepts and real-world problem framing—ideal for readers ready to move from theory to application.

Inside, the authors balance mathematical rigor with accessible storytelling, unpacking quantum algorithms, variational approaches, and quantum-classical hybrid strategies for optimization and learning tasks. Chapters feature step-by-step derivations, illustrative examples, and comparative analyses that illuminate when and why quantum techniques can outperform classical counterparts. Whether you are a graduate student, researcher, or industry practitioner, the book equips you with the tools to evaluate quantum advantages and to design practical solutions for combinatorial optimization, pattern recognition, and data-driven decision-making.

What makes this guide stand out is its applied focus: it contextualizes quantum methods within contemporary use cases across finance, logistics, materials science, and machine intelligence—making the material relevant to professionals in Europe, North America, Asia, and beyond. Readers gain not only conceptual clarity but also a roadmap for integrating quantum routines into existing workflows and research agendas.

If you’re aiming to deepen your expertise in quantum machine learning or to implement quantum optimization techniques in applied settings, this 1st Edition is an indispensable resource. Add A Practical Guide to Quantum Machine Learning and Quantum Optimization to your library today and start bridging the gap between quantum theory and tangible results.

Note: eBooks do not include supplementary materials such as CDs, access codes, etc.