Book Image

Financial Modeling Using Quantum Computing

By : Anshul Saxena, Javier Mancilla, Iraitz Montalban, Christophe Pere
5 (1)
Book Image

Financial Modeling Using Quantum Computing

5 (1)
By: Anshul Saxena, Javier Mancilla, Iraitz Montalban, Christophe Pere

Overview of this book

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.
Table of Contents (16 chapters)
1
Part 1: Basic Applications of Quantum Computing in Finance
5
Part 2: Advanced Applications of Quantum Computing in Finance
10
Part 3: Upcoming Quantum Scenario

Identifying the potentiality of advantage with QML

When the subject is QML, there are several potential applications in finance, as was outlined in the previous chapters. The following are a few examples:

  • Risk analysis and management
  • Trading
  • Fraud detection
  • Credit scoring
  • Churn prediction

It is important to note that while there are many potential applications of quantum machine learning in finance, the field is still in its early stages. It is unclear how these algorithms will perform in practice and what the companies can expect from the QML implementations. With this in mind, analyzing how to measure the success of a quantum project can be challenging.

Despite the previously described concern, one of the interesting points about exploring quantum machine learning challenges is that popular Python SDKs such as Qiskit (IBM), TensorFlow Quantum (Google), and PennyLane (Xanadu) enable the users and quantum developers to compare “apples with apples...