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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 2. Part I Analog Quantum Computing – Quantum Annealing FREE CHAPTER
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

Chapter 11
Quantum Approximate Optimisation Algorithm

As the name suggests, the Quantum Approximate Optimisation Algorithm (QAOA) is an optimisation algorithm. It is motivated by and draws upon two optimisation algorithms considered in previous chapters: AQC and VQE. From AQC it borrows the concept of solving an optimisation problem through encoding the corresponding objective function in the problem Hamiltonian and then evolving the system in such a way that the ground state of the final Hamiltonian provides the solution we are after (in a bitstring format). From VQE it borrows the variational principle applied to the parameterised quantum circuit. Roughly speaking, QAOA is a gate-model version of an optimisation solver that otherwise could have been tackled with an analog AQC approach. We can also look at QAOA as a special case of VQE with the constraints on the form of the Hamiltonian.

QAOA was introduced in the pioneering work by Farhi, Goldstone, and Gutmann [96] in 2014...

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