Quantum Computing and Quantum Machine Learning for Engineers and Developers - Tapa dura

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Jesse Van Griensven Thé obtained his Ph.D. in Computational Fluid Dynamics at the prestigious University of Waterloo.  He is currently the CEO of EigenQ, a company that develops, and commercializes quantum technologies, including Post-Quantum Cryptography and the new Quantum Internet, including software and hardware solutions.  He teaches artificial intelligence and quantum computing at the doctoral level at the Mechanical and Mechatronics Engineering (robotics) at the University of Waterloo.  Prof. Van Griensven received many international awards for his research in various fields and is author and co-author in various patents, including two covering new developments in the quantum internet.

Roydon Fraser is a graduate of Queen’s University’s, Canada, Engineering Physics, and Princeton University’s, U.S.A., Mechanical and Aerospace Engineering programs, and is currently a professor of Mechanical and Mechatronics Engineering (MME) at the University of Waterloo, Canada. Prof. Fraser has over 150 journal publications and in 2024 was recognized by Clarivate as being among the top 0.1 % of highly cited researchers.  Fraser’s vehicle research students have been recognized with 30+ external awards, while Fraser’s teaching has been recognized with Teaching Excellence awards, NSF Long Term Supervisor awards, and his past installment as the MME Teaching Chair. 

José R. Rosas-Bustos is an accomplished inventor with multiple granted patents, trade secrets, and proprietary technologies spanning cybersecurity, infrastructure, and quantum technologies. He holds an MBA and serves as CTO of EigenQ, where he leads the development of advanced quantum systems.  José has delivered executive training, graduate-level instruction, and technical workshops in quantum technologies and cybersecurity across academic, corporate, and government institutions.  He is currently a Ph.D. candidate in Mechanical and Mechatronics Engineering at the University of Waterloo, focusing his research on quantum information processing and the quantum internet.

De la contraportada

This book guides readers from the foundations of quantum mechanics through advanced quantum algorithms (such as Shor’s and Grover’s) and state-of-the-art machine learning methods. By illustrating how these concepts apply to everyday engineering challenges, ranging from complex optimization and cryptography to high-fidelity simulations, the authors equip readers with the tools they need to develop and deploy quantum-based solutions.  Incorporating practical case studies, industry-standard platforms, and tested pedagogical approaches, this resource speaks to both academic researchers and industry professionals, enabling them to seamlessly integrate quantum technologies into their projects and workflows.

• Draws on content rigorously tested at the PhD level, ensuring both scholarly depth and practical relevance;
• Features comprehensive coverage of quantum algorithms and machine learning methods tailored to engineering problems;
• Provides a hands-on, practice-oriented framework using industry-standard platforms: Qiskit, Cirq, and PennyLane.