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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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Alex Khan is an advisor, entrepreneur, and educator in quantum computing. He is CEO of ZebraKet, a Canadian startup in supply chain optimization using quantum methods. He has had roles at Chicago Quantum, where he co-authored papers on portfolio optimization using D-Wave, Harrisburg University where he taught quantum computing, and at QuantFi. He continues to be an advisor at QuSecure.nAlex is an experienced Health IT executive. He is an engineering/physics dual major and received his BSME from Purdue University, MSME from KSU, MBA with Health Sector Management from Duke University, and a certificate in quantum computing from MITxPro. He has been recognized by The Quantum Daily as one of the 126 advisors shaping the Quantum Computing Industry.
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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

Technical requirements

The source code for this chapter is available in the following GitHub repository:

https://github.com/PacktPublishing/Quantum-Computing-Experimentation-with-Amazon-Braket/tree/main/Chapter06

The concepts of vectors, orthogonality, identity matrix, matrix multiplication, tensor product, and the equations shown in this chapter come from linear algebra and trigonometry, so it might be helpful to review those concepts as needed. There are several excellent books on this subject and two are included in the Further reading section if you wish to dive deeper into the mathematics of quantum computing.

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Authors (1)

Profile icon Alex Khan
Alex Khan
LinkedIn icon Github icon
Alex Khan is an advisor, entrepreneur, and educator in quantum computing. He is CEO of ZebraKet, a Canadian startup in supply chain optimization using quantum methods. He has had roles at Chicago Quantum, where he co-authored papers on portfolio optimization using D-Wave, Harrisburg University where he taught quantum computing, and at QuantFi. He continues to be an advisor at QuSecure. Alex is an experienced Health IT executive. He is an engineering/physics dual major and received his BSME from Purdue University, MSME from KSU, MBA with Health Sector Management from Duke University, and a certificate in quantum computing from MITxPro. He has been recognized by The Quantum Daily as one of the 126 advisors shaping the Quantum Computing Industry.
Read more
See other products by Alex Khan

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