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

Introduction to the knapsack problem

The knapsack problem is a good place to start when learning how to solve real-world combinatorial optimization problems that have constraints.

There are many types of knapsack problems, but we will only focus on the 0/1 knapsack problem with integer weights. In this case, we have a certain number of items with given weights and values, and our objective is to fit as many items in the knapsack as possible to maximize the value. The items that are not added to the knapsack have a value of 0, while those that fit in the knapsack have a value of 1. Of course, this means we would want to add all the items; however, we have a constraint on the total weight that we can place in the knapsack. This makes the problem considerably harder to solve, even though many solvers can solve this efficiently.

Each item in the knapsack is represented by xi, and each has a value, vi. The objective is to maximize the total value, V, of items, xi, placed inside the...