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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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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

Determining a better global solution

How can we address both objectives and find a global solution? A mathematical treatment of creating a QUBO that represents both objectives and includes the appropriate constraint with a set cover formulation is outside the scope of this book. Please check out the Further reading section for examples and more details regarding this area. However, we can use a simple method with the techniques we have developed to find an answer.

Here are the high-level steps we will go over:

  1. Create a combined BQM matrix, Mf, with both the Ms and Mi matrices and find an appropriate way to "connect" the items to the suppliers using Ma. We are looking for a way where, if an item is picked in the solution, there is a large negative coupling value that will also cause the optimization to pick the appropriate supplier of that item (and vice versa). We will use a multiplier, A, that is large enough to ensure this condition is enforced:

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