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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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12
The Power of Parameterised Quantum Circuits

As we have seen in the previous chapters, there is a wide range of QML models based on parameterised quantum circuits. One reason for this is their tolerance to noise  [222], which is important when we work with the NISQ hardware. However, this does not fully explain the popularity of PQCs or why they are considered strong competitors to classical ML models. There must be some fundamental properties of PQCs that make them superior to their classical counterparts. In this chapter, we discuss two such properties: resistance to overfitting and larger expressive power.

Resistance to overfitting is a direct consequence of the fact that a typical PQC – one without mid-circuit measurement – can be represented by a linear unitary operator. Linear models impose strong regularisation, thus preventing overfitting. At the same time, the model remains powerful due to the mapping of the input into the higher-dimensional Hilbert...

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