Elevate your problem-solving prowess by using cutting-edge quantum machine learning algorithms in the financial domain
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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.
Table of Contents
- Quantum Computing Paradigm
- Quantum Machine Learning Algorithms
- Quantum Finance Landscape
- Derivatives Valuation
- Portfolio Valuations
- Credit Risk Analytics
- Implementation in Quantum Clouds
- HPCs and Simulators Relevance
- NISQ Quantum Hardware Evolution
- Quantum Roadmap for Banks and Fintechs