Quantum Computing Experimentation with Amazon Braket: Explore Amazon Braket quantum computing to solve combinatorial optimization problems

Augment your quantum computing journey using Amazon Braket with in-depth details of how to use Braket devices, build quantum circuits, leverage quantum annealing and solve optimization problems

Key Features

• Learn about various quantum devices and their use in solving combinatorial optimization problems
• Prepare your business to leverage the benefits of the future quantum speedup
• Implement quantum computing concepts through real-world optimization use cases

Book Description

Amazon Braket is a cloud-based pay-per-use platform for executing quantum algorithms on cutting-edge quantum computers and simulators. It is ideal for developing robust apps with the latest quantum devices.

With this book, you'll take a hands-on approach to learning how to take real-world problems and run them on quantum devices. You'll begin with an introduction to the Amazon Braket platform and learn about the devices currently available on the platform, their benefits, and their purpose. Then, you'll review key quantum concepts and algorithms critical to converting real-world problems into a quantum circuit or binary quadratic model based on the appropriate device and its capability. The book also covers various optimization use cases, along with an explanation of the code. Finally, you'll work with a framework using code examples that will help to solve your use cases with quantum and quantum-inspired technologies. Later chapters cover custom-built functions and include almost 200 figures and diagrams to visualize key concepts. You’ll be able to scan the capabilities provided by Amazon Braket and explore the functions to adapt them for specific use cases.

By the end of this book, you’ll have the tools to integrate your current business apps and AWS data with Amazon Braket to solve constrained and multi-objective optimization problems.

What you will learn

• Explore the features and uses of the Amazon Braket console and components
• Discover the benefits of quantum computing devices available on Amazon Braket, including gate quantum computers, the annealer, and simulators
• Recognize which type of quantum device is the best fit for specific use cases and scaling
• Develop your own code from a basic set of use cases dealing with real-world optimization problems
• Understand the capabilities and limitations of current quantum computing technologies
• Explore the advanced features and API functions in Amazon Braket

Who this book is for

This book is for IT practitioners, architects, and developers looking to bring the power of quantum computing to their organizations. If you are a VP of IT, CIO, VP of architecture, chief architect, solution architect, actuarial fellow, or a developer already working on other AWS services such as AWS Lambda and EC2, you'll find this book useful in exploring how to leverage Amazon Braket for real-world use cases and to move your organization towards this emerging technology. Familiarity with the basics of quantum computing and Python is required.

Table of Contents

1. Setting Up Amazon Braket
2. Braket Devices Explained
3. User Setup, Tasks, and Understanding Device Costs
4. Writing Your First Amazon Braket Code Sample
5. Using a Quantum Annealer - Developing a QUBO Function and Applying Constraints
6. Using Gate-Based Quantum Computers - Qubits and Quantum Circuits
7. Using Gate Quantum Computers - Basic Quantum Algorithms
8. Using Hybrid Algorithms - Optimization Using Gate-Based Quantum Computers
9. Running QAOA on Simulators and Amazon Braket Devices
10. Amazon Braket Hybrid Jobs, PennyLane, and Other Braket Features

(N.B. Please use the Look Inside option to see further chapters)

Alex is an advisor, entrepreneur, and educator in quantum computing. He currently heads QuantFi USA operations and is the CEO of ZebraKet, an Ontario based quantum startup in supply chain optimization. Alex has been working in quantum computing since 2018 and was the CPO at Chicago Quantum where he co-authored papers on portfolio optimization using D-Wave. He continues to be an advisor at QuSecure. As a Corporate Faculty, he teaches undergraduate to graduate level courses in quantum computing at Harrisburg University. He has participated in multiple hackathons, advised on a research paper on COVID optimization using quantum annealing, developed solutions for VR game optimization and NLP using D-Wave and has presented introductory topics in quantum computing at quantum meetups including using D-Wave and IonQ through Amazon Braket. Alex has over 20 years of experience providing IT solutions, developing products, and managing large IT initiatives in the healthcare and insurance industry. He is an engineering/physics dual major and received his BSME from Purdue University, MSME from KSU, MBA with Health Sector Management from Duke University and a certificate in quantum computing from MITxPro.