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)

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 and...