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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 this book’s GitHub repository: https://github.com/PacktPublishing/Quantum-Computing-Experimentation-with-Amazon-Braket/tree/main/Chapter11.

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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.
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See other products by Alex Khan

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