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Quantum Machine Learning and Optimisation in Finance

Quantum Machine Learning and Optimisation in Finance

By : Jacquier Antoine, Alexei Kondratyev
4.6 (19)
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Quantum Machine Learning and Optimisation in Finance

Quantum Machine Learning and Optimisation in Finance

4.6 (19)
By: Jacquier Antoine, Alexei 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)
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7
Parameterised Quantum Circuits and Data Encoding

Having built the quantum hardware, how can we use it to the maximum effect given its scale, connectivity, and fidelity rate? This question can be best answered if we split it into two parts. First, what problems are in principle solvable on NISQ computers? Second, how do we encode classical data into quantum states?

The rest of this book focuses on the first part: problems and models that can be formulated in a way that doesn’t require a massive number of qubits and that are, at least to some extent, noise tolerant. The first step in this direction is the concept of the Parameterised Quantum Circuit (PQC) as a generic quantum machine learning model.

The second part – data encoding – is equally important and relies on several practical methods described in this chapter. This is an active area of research where we can expect most of the progress to come from the quantum software side.

7.1 Parameterised Quantum Circuits...

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