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 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
- 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.
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
Professor Anshul Saxena is a quantum finance instructor at Christ University. His current research focus is abouton discovering the role of quantum computing in solving complex financial problems. He has filed three Indian patents and holds an international patent. He has authored a popular book on HR Analytics and has developed an automated Ppython library "Cognito" for data preprocessing. He has over a decade of work experience spreading across IT and financial services companies like TCS and Northern Trust in various business analytics and decision sciences roles. He has worked as a consultant and trainer with IBM ICE group and has trained more than 500 faculties pan India. Mr. Saxena has also worked as a Corporate Trainer and has conducted training on data science for more than 600 IT employees. He is a SAS certified predictive modeler and has recently completed a certificate in "Quantum computing for managers" for BIMTECH. He holds an MBA degree in Finance from IBS Bangalore and is pursuing his Ph.D. in Financial Risk Analytics
Javier Mancilla is a Senior Data Scientist, and a Quantum Business and Programming Consultant. He is a Ph.D. candidate and Master in Data Management and Innovation. He has more than 15 years of experience in digital transformation projects, withand in the last 8 years mostly dedicated to artificial intelligence, machine learning, and quantum computing, with more than 35 projects executed around these technologies. He has more than 8 certifications in quantum computing matters from institutions like MIT xPro, KAIST, IBM, Saint Petersburg University, and BIMTECH. He also was selected as one of the Top 20 Quantum Computing Linkedin Voices by Barcelonaqbit (quantum organization in Spain). Currently, he has the role of quantum machine learning advisor for different companies and organizations in Europe and Latin America and is also an I + D + i (Investigation, Development, and Innovation) evaluator for different governments in LATAM such as Chile and Paraguay
Iraitz Montalban is currently Quantum Software Engineer for Kipu Quantum GmbH and PhD candidate at the University of the Basque Country in Quantum Machine Learning. He holds several master's degrees in Mathematical modelling, Data Protection and Quantum Technologies as well. Has hold positions of responsability in large organizations as well as coordinated Innovation practices in all of then given his trajectory as a reseacrher in AI and ML disciplines and his more than 15 years of experience in this field. He activelly collaborates with different universities and education institutions designing the curriculum and teaching in programs around BigData and Advanced Analytics
Christophe Pere is an Applied Quantum Machine Learning Researcher and Lead Scientist originally from Paris, France. He has a Ph.D. in Astrophysics from Université Côte d’Azur. After his Ph.D., he left the academic world for a career in Artificial Intelligence as an Applied Industry Researcher. He learned quantum computing during his Ph.D. in his free time, starting as a passion and becoming his new career. He actively democratizes Quantum Computing to help people and companies enter this new field.
