Book Image

Quantum Computing Experimentation with Amazon Braket

By : Alex Khan
5 (1)
Book Image

Quantum Computing Experimentation with Amazon Braket

5 (1)
By: Alex Khan

Overview of this book

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.
Table of Contents (19 chapters)
1
Introduction
Free Chapter
2
Section 1: Getting Started with Amazon Braket
7
Section 2: Building Blocks for Real-World Use Cases
13
Section 3: Real-World Use Cases

Further QAOA considerations

In the previous chapter, the QAOA implementation moved through a range of parameter values, including param1 and param2. We found that, in the parameter landscape, varying costs were returned and certain pairs of parameters sometimes yielded the lowest cost. Can we use an optimizer to move toward the lowest cost more efficiently than mapping the whole parameter landscape? This will be the first area we will investigate. In addition, in the previous chapter, we stated that by repeatedly applying a combination of Z and X rotations with appropriate parameters, the probability profile can be modified more effectively so that we see the minimum cost. We will review this as well.

Full QAOA hybrid algorithm using a classical parameter optimizer

Classical optimization algorithms have various ways of evaluating the landscape for a lower cost value and continuing to move towards a minima. However, we know that sometimes these algorithms can get stuck in a local...