What this book covers
Chapter 1, Quantum Computing Paradigm, helps readers understand the challenges and limitations of digital technology and how quantum computing can help them overcome these.
Chapter 2, Quantum Machine Learning and Optimization Algorithms, considers how quantum machine learning utilizes qubits and quantum operations for specialized quantum systems to improve computational speed and data storage. This is done by algorithms in a program. This chapters explain how the quantum machine learning algorithm works in theory and in real life.
Chapter 3, Quantum Finance Landscape, helps readers understand the quantum finance landscape and the types of financial problems to which quantum computing principles can be applied.
Chapter 4, Derivatives Valuation, highlights that the valuation of derivatives is often highly complex and can only be carried out numerically—which requires a correspondingly high computing effort. This chapter examines the role of QML algorithms in derivatives valuation.
Chapter 5, Portfolio Optimization, considers portfolio management as the process of managing a group of financial securities and making ongoing decisions to meet investment objectives. Portfolio management also includes a number of steps, such as managing costs and risks, allocating assets, researching the market, and choosing securities. This chapter examines the role of QML algorithms in portfolio allocation.
Chapter 6, Credit Risk Analytics, outlines how credit risk is associated with the possibility of a client failing to meet contractual obligations, such as mortgages, credit card debts, and other types of loans. Minimizing the risk of default is a major concern for financial institutions. Machine learning models have been helping these companies to improve the accuracy of their credit risk analysis, providing a scientific method to identify potential debtors in advance. Learn how a QML algorithm can help solve this problem using real-world data.
Chapter 7, Implementation in Quantum Clouds, dicusses how the implementation of quantum machine learning and optimization architectures in productive environments, or as a backtest for current systems, is a crucial part to retrieve knowledge and start using this technology.
Chapter 8, Simulators’ and HPCs’ roles in the NISQ Era, highlights how classical means and in particular, high-performance hardware, have a key part to play in the delivery of short-term quantum advantage. In this chapter, we will explore some of the most relevant approaches in order to map the quantum-classical landscape comprehensively.
Chapter 9, NISQ Quantum Hardware Roadmap, demonstrates how Noisy Intermediate-Scale Quantum (NISQ) Hardware can evolve in various ways depending on the provider. Different approaches, ranging from fault-tolerant logical qubits to circuit knitting, could be among the early steps towards achieving fault-tolerant devices. In this chapter, we outline the key aspects of these approaches and their long-term potential.
Chapter 10, Business Implementation, underlines that knowing quantum technology does not guarantee that companies will successfully implement quantum computing with the lowest risk possible. In this chapter, we will provide helpful information for how fintech firms and banks can implement these kinds of projects without getting stuck half-way.
