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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

Fine-tuning parameters for QAOA

In the previous sections, we developed the concepts for how QAOA works and used a set of parameters that scale the rotations of the RZ, ZZ, and RX gates. It should also be apparent that rotating the angle too far will only cause it to start moving in the opposite direction of where we want it. Thus, the parameters depend on having an understanding of the problem, the size of the coefficients, and the number of times the RZ, ZZ, and RX gates are repeated. We will continue with our objective function to see whether we can improve on the parameters and get a better result:

  1. In this first example, we will set param2 to 0.1 and vary param1 using x2 while plotting the probabilities of the four possible answers. The code for this is as follows:
    x2=np.arange(0,1.5,.1)# set x
    x=x2
    prob_00=[]
    prob_01=[]
    prob_10=[]
    prob_11=[]
    c_0=objective[0,0]
    c_1=objective[1,1]
    c_01=objective[0,1]
    param2=0.1
  2. he following code uses the param_tuning_example() function...
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