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

Implementing QAOA for optimization

So far, we’ve learned how to create a binary quadratic objective function and then create a quantum circuit that will evolve by increasing the probability of the state that produces the minimum cost. We also showed how QAOA depends on parameters that must be evaluated classically. In this section, we will create a simple QAOA implementation where we can visually and efficiently determine the parameters and optimize the objective function.

In the first section of this chapter, Representing a binary quadratic function using a phase adder, we started by defining an objective function and showed that a quantum computer could sample all the values of that function while determining the minimum value with the same probability as every other value. This requires a considerable number of measurements, and the last example in that section took a while to execute. The results were shown in Figure 8.3. Now, we will use our implementation of QAOA to...