Financial Modeling Using Quantum Computing: Design and manage quantum machine learning solutions for financial analysis

Description

Elevate your problem-solving prowess by using cutting-edge quantum machine learning algorithms in the financial domain Purchase of the print or Kindle book includes a free PDF eBook Key Features Learn to solve financial analysis problems by harnessing quantum power Unlock the benefits of quantum machine learning and its potential to solve problems Train QML to solve portfolio optimization and risk analytics problems Book Description Quantum computing has the potential to revolutionize the computing paradigm. By integrating quantum algorithms with artificial intelligence and machine learning, we can harness the power of qubits to deliver comprehensive and optimized solutions for intricate financial problems. This book offers step-by-step guidance on using various quantum algorithm frameworks within a Python environment, enabling you to tackle business challenges in finance. With the use of contrasting solutions from well-known Python libraries with quantum algorithms, you'll discover the advantages of the quantum approach. Focusing on clarity, the authors expertly present complex quantum algorithms in a straightforward, yet comprehensive way. Throughout the book, you'll become adept at working with simple programs illustrating quantum computing principles. Gradually, you'll progress to more sophisticated programs and algorithms that harness the full power of quantum computing. By the end of this book, you'll be able to design, implement and run your own quantum computing programs to turbocharge your financial modelling. What you will learn Examine quantum computing frameworks, models, and techniques Get to grips with QC's impact on financial modelling and simulations Utilize Qiskit and Pennylane for financial analyses Employ renowned NISQ algorithms in model building Discover best practices for QML algorithm Solve data mining issues with QML algorithms Who this book is for This book is for financial practitioners, quantitative analysts, or developers; looking to bring the power of quantum computing to their organizations. This is an essential resource written for finance professionals, who want to harness the power of quantum computers for solving real-world financial problems. A basic understanding of Python, calculus, linear algebra, and quantum computing is a prerequisite. Cover Title Page Copyright Dedication Contributors Table of Contents Preface Part 1: Basic Applications of Quantum Computing in Finance Chapter 1: Quantum Computing Paradigm The evolution of quantum technology and its related paradigms The evolution of computing paradigms Business challenges and technology solutions Current business challenges and limitations of digital technology Basic quantum mechanics principles and their application The emerging role of quantum computing technology for next-generation businesses From quantum mechanics to quantum computing Approaches to quantum innovation Quantum computing value chain The business application of quantum computing Global players in the quantum computing domain across the value chain Building a quantum computing strategy implementation roadmap Building a workforce for a quantum leap Summary Chapter 2: Quantum Machine Learning Algorithms and Their Ecosystem Technical requirements Foundational quantum algorithms Deutsch-Jozsa algorithm Grover’s algorithm Shor’s algorithm QML algorithms Variational Quantum Classifiers Quantum neural networks Quantum Support Vector Classification (QSVC) Variational Quantum Eigensolver QAOA Quantum programming Qiskit PennyLane Cirq Quantum Development Kit (QDK) Quantum clouds IBM Quantum Amazon Braket Microsoft Quantum Summary References Chapter 3: Quantum Finance Landscape Introduction to types of financial institutions Retail banks Investment banks Investment managers Government institutions Exchanges/clearing houses Payment processors Insurance providers Key problems in financial services Asset management Risk analysis Investment and portfolios Profiling and data-driven services Customer identification and customer retention Information gap Customization Fraud detection Summary Further reading References Part 2: Advanced Applications of Quantum Computing in Finance Chapter 4: Derivative Valuation Derivatives pricing – the theoretical aspects The time value of money Case study one Securities pricing Case study two Derivatives pricing Case study three Derivatives pricing – theory The Black-Scholes-Merton (BSM) model Computational models Machine learning Geometric Brownian motion Quantum computing Implementation in Qiskit Using qGANs for price distribution loading Summary Further reading References Chapter 5: Portfolio Management Financial portfolio management Financial portfolio diversification Financial asset allocation Financial risk tolerance Financial portfolio optimization MPT The efficient frontier Example Case study Financial portfolio simulation Financial portfolio simulation techniques Portfolio management using traditional machine learning algorithms Classical implementation Quantum algorithm portfolio management implementation Quantum annealers D-Wave implementation Qiskit implementation Conclusion Chapter 6: Credit Risk Analytics The relevance of credit risk analysis Data exploration and preparation to execute both ML and QML models Features analysis Data preprocessing Real business data Synthetic data Case study Provider of the data Features Implementation of classical and quantum machine learning algorithms for a credit scoring scenario Data preparation Preprocessing Quantum Support Vector Machines QNNs VQC Classification key performance indicators Balanced accuracy, or ROC-AUC score Conclusion Further reading Chapter 7: Implementation in Quantum Clouds Challenges of quantum implementations on cloud platforms D-Wave IBM Quantum Amazon Braket Azure Cost estimation Summary Further reading References Part 3: Upcoming Quantum Scenario Chapter 8: Simulators and HPC’s Role in the NISQ Era Local simulation of noise models Tensor networks for simulation GPUs Summary Further reading References Chapter 9: NISQ Quantum Hardware Roadmap Logical versus physical qubits Fault-tolerant approaches Circuit knitting Error mitigation Annealers and other devices Summary Further reading References Chapter 10: Business Implementation The quantum workforce barrier Case study Key skills for training resources Infrastructure integration barrier Case study Identifying the potentiality of advantage with QML Case study Funding or budgeting issues Case study Market maturity, hype, and skepticism Case study Road map for early adoption of quantum computing for financial institutions Case study Quantum managers’ training Case study Conclusions References Index About Packt Other Books You May Enjoy