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Quantum Computing Experimentation with Amazon Braket

You're reading from   Quantum Computing Experimentation with Amazon Braket Explore Amazon Braket quantum computing to solve combinatorial optimization problems

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Product type Paperback
Published in Jul 2022
Publisher Packt
ISBN-13 9781800565265
Length 420 pages
Edition 1st Edition
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Author (1):
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Alex Khan Alex Khan
Author Profile Icon Alex Khan
Alex Khan
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Toc

Table of Contents (19) Chapters Close

Preface 1. Introduction
2. Section 1: Getting Started with Amazon Braket FREE CHAPTER
3. Chapter 1: Setting Up Amazon Braket 4. Chapter 2: Braket Devices Explained 5. Chapter 3: User Setup, Tasks, and Understanding Device Costs 6. Chapter 4: Writing Your First Amazon Braket Code Sample 7. Section 2: Building Blocks for Real-World Use Cases
8. Chapter 5: Using a Quantum Annealer – Developing a QUBO Function and Applying Constraints 9. Chapter 6: Using Gate-Based Quantum Computers – Qubits and Quantum Circuits 10. Chapter 7: Using Gate Quantum Computers – Basic Quantum Algorithms 11. Chapter 8: Using Hybrid Algorithms – Optimization Using Gate-Based Quantum Computers 12. Chapter 9: Running QAOA on Simulators and Amazon Braket Devices 13. Section 3: Real-World Use Cases
14. Chapter 10: Amazon Braket Hybrid Jobs, PennyLane, and other Braket Features 15. Chapter 11: Single-Objective Optimization Use Case 16. Chapter 12: Multi-Objective Optimization Use Case 17. Other Books You May Enjoy Appendix: Knapsack BQM Derivation

Concluding section 2

This section showed you the possibilities and limitations in this new field of quantum computing. We started by learning about quantum annealing and were introduced to the D-Wave advantage device, which can be used to find the minimum of a QUBO problem. Then, we slowly worked our way through understanding how quantum computers work. We started with qubits, then quantum gates, and eventually more elaborate quantum circuits that can add, subtract, and reveal numbers that are stored in the phase information of the qubits. We showed you how to use a quantum phase adder to determine all possible values of a function. This led to us creating a way to amplify the probability of the minimum cost using the QAOA hybrid algorithm, which requires classical parameter tuning. Finally, we evaluated the practical implementation of this algorithm on various devices.

In the current evolution of quantum computers, there are considerable challenges when it comes to getting results...

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