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)

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   [117] 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 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 a generalisation...