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)

Quadratic Unconstrained Binary Optimisation

Undoubtedly, Quadratic Unconstrained Binary Optimisation (QUBO) is a flagship use case of quantum annealing. We only need to have a closer look at the name of this class of optimisation problems to see why:

  • Quantum annealers operate on binary spin variables. It is straightforward to perform mapping between binary decision variables (represented by the logical qubits) and spin variables.
  • The objective functions of quadratic optimisation problems have only linear and quadratic terms. This significantly simplifies the models and allows their embedding on existing quantum annealing hardware.
  • Unconstrained optimisation means that although QUBO allows us to specify conditions that must be satisfied, they are not hard constraints. The violation of constraints is penalised through the additional terms in the QUBO objective function, but it is still possible to find solutions that violate specified constraints.

All these features make QUBO problems...