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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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8
Quantum Neural Network

Quantum neural networks  [100] are parameterised quantum circuits that can be trained as either generative or discriminative machine learning models in direct analogy with their classical counterparts. In this chapter, we will consider parameterised quantum circuits trained as classifiers. In the most general case, a classifier is a function that takes an N-dimensional input and returns one of M possible class values. The classifier can be trained on a dataset of samples with known class labels by adjusting the configurable model parameters in such a way as to minimise the classification error. Once the classifier is fully trained, it can be exposed to new unseen samples for which correct class labels are unknown. Therefore, it is critically important to avoid overfitting to the training dataset and ensure that the classifier generalises well to the new data.

There are many similarities between quantum and classical neural networks. In both cases, the...

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