New Book Announcement: Quantum Machine Learning—Theory, Algorithms, and Practical Implementation

We are pleased to announce that a new book by Dr. Hamid D. Ismail—Quantum Machine Learning: Theory, Algorithms, and Practical Implementation—is scheduled for publication later this year.

This forthcoming title brings together the foundations of quantum computing with modern machine learning, offering a clear and rigorous pathway from theory to real-world implementation. Designed for students, researchers, and practitioners, the book presents core concepts such as quantum states, circuits, and measurements, and builds toward advanced topics including variational quantum algorithms, quantum kernels, hybrid quantum-classical models, and emerging applications in data science and bioinformatics.

A distinctive feature of the book is its strong emphasis on practice. Readers will find hands-on examples and reproducible workflows using widely adopted frameworks such as Qiskit and PennyLane, along with detailed explanations that bridge mathematical insight and computational execution. The text also addresses current challenges in the field, including noise, scalability, and the transition toward fault-tolerant quantum systems.

This publication reflects Dr. Ismail’s ongoing research and teaching in high-performance computing, bioinformatics, and quantum machine learning, and aims to serve as both a classroom resource and a reference for advancing interdisciplinary research.

Further details, including release date and availability, will be shared soon. Stay tuned for updates and pre-order information.