Quantum Machine Learning and Optimisation in Finance: On the Road to Quantum Advantage

by Alexander Lipton, Antoine Jacquier, Marcos Lopez de Prado, Oleksiy Kondratyev

Length: 442 pages
Edition: 1
Language: English
Publisher: Packt Publishing
Publication Date: 2022-10-31
ISBN-10: 1801813574
ISBN-13: 9781801813570
Sales Rank: #470619 (See Top 100 Books)

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Print Book Look Inside

Learn the principles of quantum machine learning and how to apply them in finance.

Purchase of the print or Kindle book includes a free eBook in the PDF format.

Key Features

Discover how to solve optimisation problems on quantum computers that can provide a speedup edge over classical methods
Use methods of analogue and digital quantum computing to build powerful generative models
Create the latest algorithms that work on Noisy Intermediate-Scale Quantum (NISQ) computers

Book Description

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!

What you will learn

Train parameterised quantum circuits as generative models that excel on NISQ hardware
Solve hard optimisation problems
Apply quantum boosting to financial applications
Learn how the variational quantum eigensolver and the quantum approximate optimisation algorithms work
Analyse the latest algorithms from quantum kernels to quantum semidefinite programming
Apply quantum neural networks to credit approvals

Who this book is for

This book is for Quants and developers, data scientists, researchers, and students in quantitative finance. Although the focus is on financial use cases, all the methods and techniques are transferable to other areas.

Quantum Machine Learning and Optimisation in Finance
    Foreword
    Endorsements
    Contributors
        About the authors
        About the reviewer
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    Table of Contents
        Standard notations
        Standard abbreviations
    Preface
        Introduction
        Who this book is for
        What this book covers
        To get the most out of this book
        Conventions used
        Get in touch
        Share your thoughts
        Download a free PDF copy of this book
    Chapter 1:  The Principles of Quantum Mechanics
        1.1  Linear Algebra for Quantum Mechanics
        1.2  Postulates of Quantum Mechanics
        1.3  Pure and Mixed States
        Summary
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Part I:  Analog Quantum Computing – Quantum Annealing
    Chapter 2:  Adiabatic Quantum Computing
        2.1  Complexity of Computational Problems
        2.2  Principles of Adiabatic Quantum Computing
        2.3  Implementations of AQC
        2.4  Universality of AQC
        Summary
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    Chapter 3:  Quadratic Unconstrained Binary Optimisation
        3.1  Principles of Quadratic Unconstrained Binary Optimisation
        3.2  Forward and Reverse Quantum Annealing
        3.3  Discrete Portfolio Optimisation
        Summary
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    Chapter 4:  Quantum Boosting
        4.1  Quantum Annealing for Machine Learning
        4.2  QBoost Applications in Finance
        4.3  Classical Benchmarks
        Summary
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    Chapter 5:  Quantum Boltzmann Machine
        5.1  From Graph Theory to Boltzmann Machines
        5.2  Restricted Boltzmann Machine
        5.3  Training and Running RBM
        5.4  Quantum Annealing and Boltzmann Sampling
        5.5  Deep Boltzmann Machine
        Summary
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Part II:  Gate Model Quantum Computing
    Chapter 6:  Qubits and Quantum Logic Gates
        6.1  Binary Digit (Bit) and Logic Gates
        6.2  Physical Realisations of Classical Bits and Logic Gates
        6.3  Quantum Binary Digit (Qubit) and Quantum Logic Gates
        6.4  Reversible Computing
        6.5  Entanglement
        6.6  Quantum Gate Decompositions
        6.7  Physical Realisations of Qubits and Quantum Gates
        6.8  Quantum Hardware and Simulators
        Summary
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    Chapter 7:  Parameterised Quantum Circuits and Data Encoding
        7.1  Parameterised Quantum Circuits
        7.2  Angle Encoding
        7.3  Amplitude Encoding
        7.4  Binary Inputs into Basis States
        7.5  Superposition Encoding
        7.6  Hamiltonian Simulation
        Summary
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    Chapter 8:  Quantum Neural Network
        8.1  Quantum Neural Networks
        8.2  Training QNN with Gradient Descent
        8.3  Training QNN with Particle Swarm Optimisation
        8.4  QNN Embedding on NISQ QPU
        8.5  QNN Trained as a Classifier
        8.6  Classical Benchmarks
        8.7  Improving Performance with Ensemble Learning
        Summary
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    Chapter 9:  Quantum Circuit Born Machine
        9.1  Constructing QCBM
        9.2  Differentiable Learning of QCBM
        9.3  Non-Differentiable Learning of QCBM
        9.4  Classical Benchmark
        9.5  QCBM as a Market Generator
        Summary
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    Chapter 10:  Variational Quantum Eigensolver
        10.1  The Variational Approach
        10.2  Calculating Expectations on a Quantum Computer
        10.3  Constructing the PQC
        10.4  Running the PQC
        10.5  Discrete Portfolio Optimisation with VQE
        Summary
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    Chapter 11:  Quantum Approximate Optimisation Algorithm
        11.1  Time Evolution
        11.2  The Suzuki-Trotter Expansion
        11.3  The Algorithm Specification
        11.4  The Max-Cut Problem
        Summary
    Chapter 12:  The Power of Parameterised Quantum Circuits
        12.1  Strong Regularisation
        12.2  Expressive Power
        Summary
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    Chapter 13:  Looking Ahead
        13.1  Quantum Kernels
        13.2  Quantum Generative Adversarial Networks
        13.3  Bayesian Quantum Circuit
        13.4  Quantum Semidefinite Programming
        13.5  Beyond NISQ
        Summary
    Bibliography
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    Index

AI & Machine Learning Data Modeling & Design Mathematical Analysis Mathematics Neural Networks

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