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

Benchmarking QAOA on Amazon Braket devices

In this section, we will use what we have learned with QAOA to compare the performance of Amazon Braket devices. This includes the SV1 and TN1 quantum simulators, IonQ’s 11-qubit Ion Trap quantum computer, Rigetti’s new Aspen-11 38-qubit superconducting quantum processor, D-Wave’s quantum annealer, and the classical simulated annealer, which is also available through D-Wave. We will start with an 11x11 matrix that represents our problem and work our way up to a 100x100 matrix. Along the way, we will discover the strategies that are needed to solve these types of matrices on different quantum devices and the limits of each device.

Optimizing an 11x11 matrix

We will continue to use the IonQ_matrix.csv file in this section. The IonQ device that’s available on Amazon Braket has 11 qubits, so we cannot solve a matrix larger than this with the available device. However, in the IonQ device, every qubit can be entangled...