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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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Toc

Table of Contents (21) Chapters Close

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

ChapterĀ 3
Quadratic Unconstrained Binary Optimisation

Undoubtedly, Quadratic Unconstrained Binary Optimisation (QUBO) is a flagship use case of quantum annealing. We only need to have a closer look at the name of this class of optimisation problems to see why:

  • Quantum annealers operate on binary spin variables. It is straightforward to perform mapping between binary decision variables (represented by the logical qubits) and spin variables.
  • The objective functions of quadratic optimisation problems have only linear and quadratic terms. This significantly simplifies the models and allows their embedding on existing quantum annealing hardware.
  • Unconstrained optimisation means that although QUBO allows us to specify conditions that must be satisfied, they are not hard constraints. The violation of constraints is penalised through the additional terms in the QUBO objective function, but it is still possible to find solutions that violate specified constraints.

All these features make...

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