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

Circuit knitting

Circuit knitting was proposed recently (Piveteau and Sutter 2022), given the complexity of providing larger chips without introducing large amounts of errors. Instead of aiming for larger, fully quantum chips, you could think of distributed resource systems where these instances are classically connected.

This type of architecture has been exploited in the field of distributed GPU computing (Gu et al. 2019), distributed computing for big data (Zaharia et al. 2012), and even edge computation (Shi 2016). However, it does not entail a paradigm shift from classical to quantum as all these resources work, let’s say, at the same physical level.

The main difference between those approaches and circuit knitting is the need to split a quantum circuit that would classically communicate with other parts of the circuit. Assuming there is a group of gates that could minimize the cut between two groups of more densely connected operations, you could split the circuit...