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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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Index

A

activation functions 192

activation unit 186

AdaBoost 167

addition operator

    building from NAND gates 272

Adiabatic Quantum Computing (AQC) 88

    implementation 111

    power 96

    principles 92

    universality 124

adjustable one-qubit gates 295

adjustable two-qubit gates 304

amplitude estimation 679

analytic gradient approach 414

ancillary qubits 389, 651

AND gate 268

ansatz 416, 439, 531, 534

Artificial Neural Network (ANN) 186

artificial neuron 186

B

backpropagation 190

bagging 446

Bayesian Quantum Circuit (BQC) 650

Bell circuit 318

Bell states 318

binary digit (bit) 43, 267

binary integer linear programming problem 137

Black-Scholes parabolic PDE 689

Bloch sphere 287

Boltzmann distribution 209, 223

Boltzmann sampling 240

bootstrap 446

C

Chimera graph 120, 251, 699

classical benchmarking 154, 186, 444, 484

client clustering 578

client segmentation 578

CMOS...

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