As quantum machine learning (QML) continues to evolve, many professionals struggle to apply its powerful algorithms to real-world problems using noisy intermediate-scale quantum (NISQ) hardware. This book bridges that gap by focusing on hands-on QML applications tailored to NISQ systems, moving beyond the traditional textbook approaches that explore standard algorithms like Shor's and Grover's, which lie beyond current NISQ capabilities.
You’ll get to grips with major QML algorithms that have been widely studied for their transformative potential in finance and learn hybrid quantum-classical computational protocols, the most effective way to leverage quantum and classical computing systems together.
The authors, Antoine Jacquier, a distinguished researcher in quantum computing and stochastic analysis, and Oleksiy Kondratyev, a Quant of the Year awardee with over 20 years in quantitative finance, offer a hardware-agnostic perspective. They present a balanced view of both analog and digital quantum computers, delving into the fundamental characteristics of the algorithms while highlighting the practical limitations of today’s quantum hardware.
By the end of this quantum book, you’ll have a deeper understanding of the significance of quantum computing in finance and the skills needed to apply QML to solve complex challenges, driving innovation in your work.

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Spis treści

Quantum Machine Learning and Optimisation in Finance. Drive financial innovation with quantum-powered algorithms and optimisation strategies - Second Edition eBook -- spis treści

• 1. The Principles of Quantum Mechanics
• 2. Adiabatic Quantum Computing
• 3. Quadratic Unconstrained Binary Optimisation
• 4. Quantum Boosting
• 5. Quantum Boltzmann Machine
• 6. Qubits and Quantum Logic Gates
• 7. Parameterised Quantum Circuits and Data Encoding
• 8. Quantum Neural Network
• 9. Quantum Circuit Born Machine
• 10. Variational Quantum Eigensolver
• 11. Quantum Approximate Optimisation Algorithm
• 12. Quantum Kernels and Quantum Two-Sample Test
• 13. The Power of Parameterised Quantum Circuits
• 14. Advanced QML Models
• 15. Beyond NISQ