Quantum Machine Learning And Optimisation In Finance
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Quantum Machine Learning And Optimisation In Finance
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Author : Antoine Jacquier
language : en
Publisher: Packt Publishing Ltd
Release Date : 2022-10-31
Quantum Machine Learning And Optimisation In Finance written by Antoine Jacquier and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-10-31 with Mathematics categories.
Learn the principles of quantum machine learning and how to apply them While focus is on financial use cases, all the methods and techniques are transferable to other fields Purchase of Print or Kindle includes a free eBook in PDF 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.
Financial Modeling Using Quantum Computing
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Author : Anshul Saxena
language : en
Publisher: Packt Publishing Ltd
Release Date : 2023-05-31
Financial Modeling Using Quantum Computing written by Anshul Saxena and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-05-31 with Business & Economics categories.
Achieve optimized solutions for real-world financial problems using quantum machine learning algorithms 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 DescriptionQuantum 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 Explore framework, model and technique deployed for Quantum Computing Understand the role of QC in financial modeling and simulations Apply Qiskit and Pennylane framework for financial modeling Build and train models using the most well-known NISQ algorithms Explore best practices for writing QML algorithms Use QML algorithms to understand and solve data mining problems 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.
Financial Modeling Using Quantum Computing
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Author : Anshul Saxena
language : en
Publisher:
Release Date : 2023-03
Financial Modeling Using Quantum Computing written by Anshul Saxena and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-03 with categories.
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.
Applications Of Quantum Computers In Financial Markets
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Author : Pooyan Ghamari
language : en
Publisher: Pooyan Ghamari
Release Date : 2023-03-14
Applications Of Quantum Computers In Financial Markets written by Pooyan Ghamari and has been published by Pooyan Ghamari this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-03-14 with Computers categories.
The ebook "Applications of Quantum Computers in Financial Markets" by Pooyan Ghamari is a comprehensive guide to the potential applications of quantum computing in the finance industry. The book is organized into nine sections that cover topics such as understanding quantum computing, quantum computing in finance, portfolio optimization, risk management, asset pricing, quantum key distribution, quantum machine learning, challenges and limitations, and regulatory and ethical considerations. In the introduction, the author provides a brief overview of the current state of financial markets and the potential benefits of quantum computing. The first section provides an introduction to quantum mechanics and its relevance to computing, explaining quantum bits (qubits) and quantum gates. The second section explores how quantum computing can be used in financial markets. It provides an overview of the different types of financial applications such as portfolio optimization, risk management, and asset pricing. The following three sections delve into each of these applications in detail. Section three explains portfolio optimization and how quantum computing can be used for it. Section four explains risk management and how quantum computing can be used for it. Section five explains asset pricing and how quantum computing can be used for it. Section six covers quantum key distribution, which is a secure communication method that uses quantum cryptography to ensure data privacy and security in financial transactions. Section seven covers quantum machine learning, which is a subset of machine learning that leverages quantum computing to speed up the training process and enhance the accuracy of machine learning algorithms in finance. Section eight covers the current limitations of quantum computing technology, the challenges of implementing quantum computing in financial markets, and the potential risks associated with it. Section nine covers the regulatory landscape for quantum computing in finance and the ethical considerations associated with its use. The conclusion summarizes the potential benefits and challenges of using quantum computing in finance and discusses future directions for research and development in this area. The references section provides a list of sources used in the ebook, including academic papers, industry reports, and news articles.
Ai Quantum Computing For Finance Insurance Fortunes And Challenges For China And America
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Author : Lee David Kuo Chuen
language : en
Publisher: World Scientific
Release Date : 2019-04-16
Ai Quantum Computing For Finance Insurance Fortunes And Challenges For China And America written by Lee David Kuo Chuen and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-04-16 with Business & Economics categories.
This book offers a framework and analysis for the current technological landscape between the United States and China across the financial and insurance sectors as well as emerging technologies such as AI, Blockchain, Cloud and Data Analytics and Quantum Computing (ABCDQ). Based on original lecture slides used by the authors, the book presents contemporary and critical views of emergent technologies for a wide spectrum of readers from CEOs to university lecturers to students. The narrative aims to help readers upgrade their technology literacy and to overcome the fear of AI posed by our lizard brain.
Machine Learning With Quantum Computers
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Author : Maria Schuld
language : en
Publisher: Springer Nature
Release Date : 2021-10-17
Machine Learning With Quantum Computers written by Maria Schuld and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-10-17 with Science categories.
This book offers an introduction into quantum machine learning research, covering approaches that range from "near-term" to fault-tolerant quantum machine learning algorithms, and from theoretical to practical techniques that help us understand how quantum computers can learn from data. Among the topics discussed are parameterized quantum circuits, hybrid optimization, data encoding, quantum feature maps and kernel methods, quantum learning theory, as well as quantum neural networks. The book aims at an audience of computer scientists and physicists at the graduate level onwards. The second edition extends the material beyond supervised learning and puts a special focus on the developments in near-term quantum machine learning seen over the past few years.
Quantum Machine Learning
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Author : Peter Wittek
language : en
Publisher: Academic Press
Release Date : 2014-09-10
Quantum Machine Learning written by Peter Wittek and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-09-10 with Computers categories.
Quantum Machine Learning bridges the gap between abstract developments in quantum computing and the applied research on machine learning. Paring down the complexity of the disciplines involved, it focuses on providing a synthesis that explains the most important machine learning algorithms in a quantum framework. Theoretical advances in quantum computing are hard to follow for computer scientists, and sometimes even for researchers involved in the field. The lack of a step-by-step guide hampers the broader understanding of this emergent interdisciplinary body of research. Quantum Machine Learning sets the scene for a deeper understanding of the subject for readers of different backgrounds. The author has carefully constructed a clear comparison of classical learning algorithms and their quantum counterparts, thus making differences in computational complexity and learning performance apparent. This book synthesizes of a broad array of research into a manageable and concise presentation, with practical examples and applications. Bridges the gap between abstract developments in quantum computing with the applied research on machine learning Provides the theoretical minimum of machine learning, quantum mechanics, and quantum computing Gives step-by-step guidance to a broader understanding of this emergent interdisciplinary body of research
Mastering Quantum Analysis For Financial Growth
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Author : Marcel Souza
language : en
Publisher: GAVEA LAB
Release Date :
Mastering Quantum Analysis For Financial Growth written by Marcel Souza and has been published by GAVEA LAB this book supported file pdf, txt, epub, kindle and other format this book has been release on with Business & Economics categories.
Unlock the untapped potential of quantum analysis and skyrocket your financial success with this groundbreaking book – "MASTERING QUANTUM ANALYSIS FOR FINANCIAL GROWTH" In this revolutionary guide, you'll dive deep into the world of quantum finance and discover how cutting-edge technologies can empower you to make smarter investment decisions. Whether you're a seasoned investor or a curious beginner, this comprehensive book will equip you with the knowledge and strategies to thrive in the dynamic world of quantum analysis. Embrace the power of quantum computing and gain a competitive edge by leveraging its processing capabilities to analyze complex financial data. Learn how to harness quantum algorithms to detect market patterns, forecast trends, and identify lucrative opportunities that traditional methods often overlook. Discover how to decode the language of quantum mechanics and apply its principles to interpret financial markets in a whole new light. This book will demystify the complexities of quantum analysis and present them in a digestible and actionable format, making it accessible to everyone. Learn how to use quantum computing to optimize portfolio management, risk assessment, and asset allocation. By harnessing the power of quantum analysis, you can achieve higher returns and safeguard your investments like never before. Explore the intersection of quantum physics and behavioral finance, gaining insights into human decision-making biases and using this knowledge to your advantage. With a deeper understanding of the psychological factors driving market movements, you'll be better equipped to navigate market volatility and capitalize on opportunities. Tap into the potential of quantum machine learning and uncover hidden patterns in financial data that can lead to breakthrough investment strategies. By combining quantum computing with machine learning, you'll be at the forefront of the financial revolution. From cryptocurrency trading to traditional stock markets, "MASTERING QUANTUM ANALYSIS FOR FINANCIAL GROWTH" covers a wide range of financial instruments, allowing you to apply quantum principles to any investment avenue. Unleash the power of quantum analysis and stay ahead of the curve in the fast-paced world of finance. This book will guide you through the intricate world of quantum finance, providing you with the tools to make informed and profitable decisions. Prepare to revolutionize your investment approach and embark on a journey of financial prosperity with "MASTERING QUANTUM ANALYSIS FOR FINANCIAL GROWTH" Embrace the future of finance, where quantum computing meets savvy investment strategies, and unlock the door to limitless opportunities. The potential for financial success is waiting – are you ready to seize it?
Supervised Learning With Quantum Computers
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Author : Maria Schuld
language : en
Publisher: Springer
Release Date : 2018-10-12
Supervised Learning With Quantum Computers written by Maria Schuld and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-10-12 with Science categories.
This book investigates how quantum computers can be used for data-driven prediction. It summarizes and conceptualizes ideas that have been proposed in the discipline of quantum machine learning to provide a starting point for those new to the field, while serving as a reference for readers familiar with the topic. Given the interdisciplinary nature of the subject, the first chapters work through a simple but illustrative quantum machine learning algorithm and give a detailed overview of the parent disciplines. The book then presents core methods for the design of quantum machine learning algorithms with a focus on supervised learning. Amongst these methods are the representation of data by quantum states, quantum routines for inference and training, learning with quantum models, as well as near-term applications. The book contributes to research in quantum machine learning and targets an interdisciplinary audience of computer scientists and physicists from a graduate level onwards.
Concise Guide To Quantum Machine Learning
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Author : Davide Pastorello
language : en
Publisher: Springer Nature
Release Date : 2022-12-16
Concise Guide To Quantum Machine Learning written by Davide Pastorello and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-12-16 with Computers categories.
This book offers a brief but effective introduction to quantum machine learning (QML). QML is not merely a translation of classical machine learning techniques into the language of quantum computing, but rather a new approach to data representation and processing. Accordingly, the content is not divided into a “classical part” that describes standard machine learning schemes and a “quantum part” that addresses their quantum counterparts. Instead, to immerse the reader in the quantum realm from the outset, the book starts from fundamental notions of quantum mechanics and quantum computing. Avoiding unnecessary details, it presents the concepts and mathematical tools that are essential for the required quantum formalism. In turn, it reviews those quantum algorithms most relevant to machine learning. Later chapters highlight the latest advances in this field and discuss the most promising directions for future research. To gain the most from this book, a basic grasp of statistics and linear algebra is sufficient; no previous experience with quantum computing or machine learning is needed. The book is aimed at researchers and students with no background in quantum physics and is also suitable for physicists looking to enter the field of QML.