Productizing Quantum Computing (eng). Dieser Artikel ist nicht verfügbar.Productizing Quantum Computing (eng)

Leverage the benefits of quantum computing by identifying business use cases and understanding how to design and develop quantum products and services. This book will guide you to effectively productize quantum computing, including best practices, recommendations, and proven methods to help you navigate the challenges and risks of this emerging technology.

The book starts with a thorough introduction to quantum computing, followed by its various algorithms and applications. You will then learn how to build a strong foundation in classical computing, seek practical experience, and stay up-to-date with the latest developments in the field. Moving forward, you will gain an understanding of how to conduct market research to identify business opportunities for quantum computing products and services. The authors then guide you through the process of developing a quantum roadmap and integrating quantum computing into an existing system. This is concluded by a demonstration of howto manage quantum computing projects and how to address their risks and challenges. 

After reading this book, you will understand quantum computing and how it can be applied to real-world business problems.

What You Will Learn

• Identify business use cases for quantum computing and understand the potential benefits and risks of quantum applications
• Design and develop quantum products and services by identifying quantum algorithms, programming in quantum languages, and leveraging quantum simulators and hardware
• Integrate quantum computing into existing systems
• Integrate quantum algorithms with classical algorithms

Who This Book Is For

Product managers, developers, and entrepreneurs who wish to use the potential of quantum computing for their businesses.

Dhairyya Agarwal is a product manager at Microsoft. He is based out of Mountain View, California where he is honing his product management skill set by solving problems in the email and collaboration security space. He received his master’s degree from Carnegie Mellon University in Software Management. He has delivered on many initiatives focused on improving retention, activation, and engagement. He has also worked across 0 to 1 products to enable businesses to enter a new markets. 

Shalini D is a quantum AI researcher at Fractal QuantumAI Lab researching quantum algorithms at the intersection of quantum chemistry and quantum machine learning. Previously, she worked at Infosys as a systems engineer, where she worked as a core customization API developer. She has completed her naster's in quantum technology from CSIC-Universidad Internacional Menendez Pelayo, Spain. Her research areas include semiconductor spin qubits, quantum machine learning, and quantum chemistry. She has taught quantum computing, from basic to advanced levels, at many universities, including Vellore Institute of Technology, India. She has also delivered several talks at various universities. Her Udemy course on the Qiskit Developer Exam is the highest-rated and bestseller globally.

Srinjoy Ganguly is a quantum AI research scientist at Fractal QuantumAI Lab and a clinical Professor of Practice for Quantum Technology at Woxsen University. He is also an Associate Supervisor (adjunct) at the University of Southern Queensland in Australia, supervising PhD students in Quantum Machine Learning. He is an IBM Qiskit Advocate and an IBM Quantum Educator with over 5+ years of experience in quantum technologies. He is the author of the book Quantum Computing with Silq Programming, and has published several research papers in quantum chemistry, quantum machine learning, and quantum NLP. He possesses a triple master's in Quantum Technologies, Quantum Computing Technology and Artificial Intelligence (AI)  from CSIC-UIMP, Spain, Universidad Politecnica de Madrid and the University of Southampton, respectively. His research interests include superconducting quantum circuits, hybrid superconducting-semiconducting heterostructures (1D & 2D), topological quantum computing, spin qubits, quantum machine learning, quantum chemistry, quantum natural language processing and quantum AI.