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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

Chapter 2
Adiabatic Quantum Computing

Search algorithms are among the most important and fundamental algorithms in computer science, the most basic example being that of finding one special item among a list of N items. Classical algorithms are known to solve this problem in time proportional to the problem size, N, which becomes highly untractable when the latter grows large. In 1996, Grover [124] devised a quantum algorithm to solve such search problems with a quadratic speedup, with the obvious caveat that quantum computers did not exist at the time. Soon after, Farhi, Goldstone, Gutmann, and Sipser [98] recast the Grover problem as a satisfiability problem in the context of quantum computation by adiabatic evolution.

Another class of problems that are hard to solve classically is that of combinatorial optimisation problems. The truck dispatching problem, originally proposed by Dantzig and Ramser [78], searches the optimal routing of delivery trucks, and is...

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