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Learn Quantum Computing with Python and IBM Quantum Experience

You're reading from   Learn Quantum Computing with Python and IBM Quantum Experience A hands-on introduction to quantum computing and writing your own quantum programs with Python

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Product type Paperback
Published in Sep 2020
Publisher Packt
ISBN-13 9781838981006
Length 510 pages
Edition 1st Edition
Languages
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Author (1):
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Robert Loredo Robert Loredo
Author Profile Icon Robert Loredo
Robert Loredo
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Table of Contents (21) Chapters Close

Preface 1. Section 1: Tour of the IBM Quantum Experience (QX)
2. Chapter 1: Exploring the IBM Quantum Experience FREE CHAPTER 3. Chapter 2: Circuit Composer – Creating a Quantum Circuit 4. Chapter 3: Creating Quantum Circuits using Quantum Lab Notebooks 5. Section 2: Basics of Quantum Computing
6. Chapter 4: Understanding Basic Quantum Computing Principles 7. Chapter 5: Understanding the Quantum Bit (Qubit) 8. Chapter 6: Understanding Quantum Logic Gates 9. Section 3: Algorithms, Noise, and Other Strange Things in Quantum World
10. Chapter 7: Introducing Qiskit and its Elements 11. Chapter 8: Programming with Qiskit Terra 12. Chapter 9: Monitoring and Optimizing Quantum Circuits 13. Chapter 10: Executing Circuits Using Qiskit Aer 14. Chapter 11: Mitigating Quantum Errors Using Ignis 15. Chapter 12: Learning about Qiskit Aqua 16. Chapter 13: Understanding Quantum Algorithms 17. Chapter 14: Applying Quantum Algorithms 18. Assessments 19. Other Books You May Enjoy Appendix A: Resources

Estimating T2 decoherence times

We estimate the T2 time based on experiment results from t2_circuits executed on noisy devices. The estimate is based on the probability formula of measuring 0 from the following equation, where A, T2, and B are unknown parameters:

Finally, to estimate T2* and characterize the qubit with respect to the results, we will leverage T2Fitter. To generate the T2Fitter class, we will use similar parameter definitions as T1Fitter in the previous section, only this time, we will use the results from the T2 test circuits:

# Generate the T2Fitter class using similar parameters as the # T1Fitter
t2echo_fit = T2Fitter(t2echo_backend_result, t2echo_delay_times, qubits, fit_p0=[0.5, t2, 0.5], fit_bounds=([-0.5, 0, -0.5], [1.5, 40, 1.5]))
# Print and plot the results
print(t2echo_fit.params) 
t2echo_fit.plot(0)
plt.show()

The preceding code prints out the estimate values for A, T2, and B for qubit 0:

{'0': [array([ 0.52397653, 27.06685838,...
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