Book Image

Quantum Machine Learning and Optimisation in Finance

By : Antoine Jacquier, Oleksiy Kondratyev
Book Image

Quantum Machine Learning and Optimisation in Finance

By: Antoine Jacquier, Oleksiy Kondratyev

Overview of this book

With recent advances in quantum computing technology, we finally reached the era of Noisy Intermediate-Scale Quantum (NISQ) computing. NISQ-era quantum computers are powerful enough to test quantum computing algorithms and solve hard real-world problems faster than classical hardware. Speedup is so important in financial applications, ranging from analysing huge amounts of customer data to high frequency trading. This is where quantum computing can give you the edge. Quantum Machine Learning and Optimisation in Finance shows you how to create hybrid quantum-classical machine learning and optimisation models that can harness the power of NISQ hardware. This book will take you through the real-world productive applications of quantum computing. The book explores the main quantum computing algorithms implementable on existing NISQ devices and highlights a range of financial applications that can benefit from this new quantum computing paradigm. This book will help you be one of the first in the finance industry to use quantum machine learning models to solve classically hard real-world problems. We may have moved past the point of quantum computing supremacy, but our quest for establishing quantum computing advantage has just begun!
Table of Contents (4 chapters)


In this chapter, we learned the key principles of quantum mechanics, starting with a review of the basic elements of linear algebra, followed by an introduction to Dirac notations.

We then covered the main postulates of quantum mechanics and their relevance to quantum computing. We learned how to describe the state (statics) and the evolution (dynamics) of a closed system, the interactions of a system with external systems (measurement), observables, as well as the state of a composite system in terms of its component parts.

We finally introduced the density operator, which allows us to describe both pure and mixed quantum states, contrasting with the state vector, which can only represent pure quantum states.

In the next chapter, we will look at an application of the principles of quantum mechanics to analog quantum computing – quantum annealing.