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Quantum Machine Learning and Optimisation in Finance

You're reading from   Quantum Machine Learning and Optimisation in Finance Drive financial innovation with quantum-powered algorithms and optimisation strategies

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Product type Paperback
Published in Dec 2024
Publisher Packt
ISBN-13 9781836209614
Length 494 pages
Edition 2nd Edition
Languages
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Authors (2):
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Jacquier Antoine Jacquier Antoine
Author Profile Icon Jacquier Antoine
Jacquier Antoine
Alexei Kondratyev Alexei Kondratyev
Author Profile Icon Alexei Kondratyev
Alexei Kondratyev
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Table of Contents (21) Chapters Close

Preface 1. Chapter 1 The Principles of Quantum Mechanics FREE CHAPTER 2. Part I Analog Quantum Computing – Quantum Annealing
3. Chapter 2 Adiabatic Quantum Computing 4. Chapter 3 Quadratic Unconstrained Binary Optimisation 5. Chapter 4 Quantum Boosting 6. Chapter 5 Quantum Boltzmann Machine 7. Part II Gate Model Quantum Computing
8. Chapter 6 Qubits and Quantum Logic Gates 9. Chapter 7 Parameterised Quantum Circuits and Data Encoding 10. Chapter 8 Quantum Neural Network 11. Chapter 9 Quantum Circuit Born Machine 12. Chapter 10 Variational Quantum Eigensolver 13. Chapter 11 Quantum Approximate Optimisation Algorithm 14. Chapter 12 Quantum Kernels and Quantum Two-Sample Test 15. Chapter 13 The Power of Parameterised Quantum Circuits 16. Chapter 14 Advanced QML Models 17. Chapter 15 Beyond NISQ 18. Bibliography
19. Index 20. Other Books You Might Enjoy

5.4 Quantum Annealing and Boltzmann Sampling

The application of quantum annealing to Boltzmann sampling is based on the direct correspondence between the RBM energy function given by (5.2.4) and the Hamiltonian in quantum annealing. Recall from Chapter 2 that quantum annealing is based on the principles of adiabatic evolution from the initial state at t = 0 given by a Hamiltonian H0 to a final state at t = T given by a Hamiltonian HF , such that the system Hamiltonian at time t [0,T] is given by

H (t) = r(t)H0 + (1− r(t))HF ,

where r(t) decreases from 1 to 0 as t goes from 0 to T. An ideal adiabatic evolution scenario envisages the system always staying in the ground state of H(t): if the system starts in the ground state of H0 and the evolution proceeds slowly enough to satisfy the conditions of the quantum adiabatic theorem (Chapter 2), then the system will end up in the ground state of HF .

In practice, existing quantum annealing...

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