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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 the conflict based on the opposing objectives

Now, we want to loop through the steps in the previous section to find better results for scenario A and then work backward to find reasonably good values for scenario B. Please refer to Figure 12.2, which summarizes these steps. First, we will do this with the classical probabilistic solver and then use D-Wave.

Evaluating the results with the probabilistic solver

We can quickly visualize and then evaluate the conflict by looping through the steps multiple times and plotting the results of each scenario. Here, we will continue to use the probabilistic solver:

  1. The following code is for scenario A and accumulates supplier and inventory energy values for plotting. We will repeat the process 20 times, as set in the total variable. The code lines should be familiar from the previous section. At the end of the code, we will convert the item values from the m_i matrix into actual product numbers that are stored in i_item...
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