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Financial Modeling Using Quantum Computing
Financial Modeling Using Quantum Computing

Financial Modeling Using Quantum Computing: Design and manage quantum machine learning solutions for financial analysis and decision making

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Profile Icon Anshul Saxena Profile Icon Javier Mancilla Profile Icon Iraitz Montalban Profile Icon Christophe Pere
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eBook May 2023 292 pages 1st Edition
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Financial Modeling Using Quantum Computing

Quantum Computing Paradigm

Quantum computers have shown the potential to be game-changing for large-scale industries in the near future. Quantum solutions (hardware and software), in their prime, have the potential to put humankind on planet Pluto with their optimized calculations. According to a Gartner report, 20% of organizations will be budgeting for quantum computing projects by 2023 (The CIO’s Guide to Quantum Computing, https://tinyurl.com/yrk4rp2u). This technology promises to achieve better accuracy and deliver real-world experiences via simulations. This book delves into the potential applications of quantum solutions to solve real-world financial problems.

In this chapter, we will discuss various computing paradigms currently in the research phase. A chronicle of quantum computing is also curated and presented. Then, we will cover the limitations faced by classical computers and how these challenges will be overcome with the help of quantum computers. After that, the role of quantum computing in shaping the next generation of business is defined.

Later in the chapter, we will go through the basics of quantum computing. The types of hardware powering quantum computers are described in a subsequent section. We will also look into the potential business applications of this technology and how organizations can align their business strategy accordingly to harness their true potential.

The following topics will be covered in this chapter:

  • The evolution of quantum technology and its related paradigms
  • Basic quantum mechanics principles and their application
  • The business applications of quantum computing

The evolution of quantum technology and its related paradigms

Computing paradigms can be defined as the significant milestones that have been achieved over the years. To say that computers have made the lives of humans easier is an understatement. On a daily basis, we need machines that can analyze, simulate, and optimize solutions to complex problems. Although the shapes and sizes of computers have changed over time, they still operate on the doctrines proposed by Alan Turing and John von Neumann.

In this section, we will study the evolution of quantum technology over the years. We will also study some of the technology’s limitations in the face of certain business challenges.

The evolution of computing paradigms

Turing showed us the types of problems computers can solve, von Neumann built programmable computers, and Michael Moore’s pioneering work in semiconductors made computers more capable. Figure 1.1 shows the advancement of computing paradigms over the years, and their ability to affect growth in human history:

1821

Mechanical calculator

Has Enabled humans to migrate from mechanical devices to electronic devices with better accuracy in calculations.

1890

Punch-card system

Demonstrated first use case of large-scale computing by aiding in the US Census.

1936

Turing machine

Theoretical conceptual framework was laid down to solve large computational problems.

1941

Digital electronic computer

First time a computer was able to store information on its main memory.

1945

Electronic Numerical Integrator and Calculator (ENIAC)

First digital computer to perform large class of numerical problems through reprogramming.

1958

Integrated Circuit (IC)

Helped in the transition of enterprise-level computing to personal computing.

1976

Cray-1 Supercomputer

Aided 240 million calculations useful for large-scale scientific applications and simulations.

1997

Parallel computing

Multiple-CPU core was used to solve complex problems in a limited timeframe, enabling Google to form a better search engine.

2006

Cloud computing

Technology has enabled users to access large computational resources from remote locations.

2016

Reprogrammable quantum computer

Offers a better platform to solve complex simulation or optimization problems in comparison to classical computers

2017

Molecular informatics

Harnesses molecular properties for rapid, scalable information storage and processing.

Figure 1.1 – Evolution of computing paradigms

The evolution of computing technology has enabled humans to evolve from an agrarian society to an industrial society. Progress in computing prowess has catapulted society from bartering goods to building e-commerce platforms. Figure 1.1 has given a conclusive summary of how computing technology has benefitted society through its progression from a device that merely performs calculations to the multifunction device in its present form. In the next section, we are going to assess the challenges faced by large-scale businesses and the limitations of current digital technology in addressing them.

Business challenges and technology solutions

Current digital technologies have advantages as well as limitations in providing solutions and insights in real time. The rise of numerous variables and their increasing complexity can affect decision-making in the real world. It is essential to have technology that is reliable and accurate, and fast-paced at the same time. The need for a reliable technology stack has prompted scientists worldwide to investigate technology that is beyond the reach of humans. The current challenges faced by large-scale businesses are as follows:

  • Faster task completion: In the current era, where manufacturing firms are looking to achieve super-large-scale production capacity and efficiency, there is a need to build faster and more reliable systems. For instance, according to an exciting study by Artificial Brain (How Artificial Brain is Building an Optimal Algorithm for EV Charger Placement Using Quantum Annealing and a Genetic Algorithm, Quantum Zeitgeist, https://tinyurl.com/bdep5eze) regarding setting up charging stations within a 50-mile radius in the San Francisco Bay Area, around 8,543,811,434,435,330 combinations were possible. Now, how can this distribution be optimized when such a large number of combinations is possible? A quantum computer theoretically solved this problem in less than 3 seconds.
  • Content discovery: With the advent of social media websites, a plethora of content is available to analyze. This content is available in different sizes and shapes, in the form of text and images. An organization would need a computer with superior computing power to explore this content. This special computing prowess was achieved through parallel computing and local optimization of the machines. However, much needs to be achieved in this field in order to mine real-time business insights from the underlying data. Quantum natural language processing (QNLP) is a promising technique to resolve problems in real time.
  • Lower administration costs: It is always a good strategy to optimize costs. Automation of mega factories has provided the owners with a solution in the right direction. Large-scale automation comes with a set of problems of its own, but precision and real-time decision-making help to make it more accurate and reliable. Recently, BMW has come up with a challenge where competitors have to focus on solving problems based on pre-production vehicle configuration, material deformation in production, vehicle sensor placement, and machine learning for automated quality assessment. Based on the results obtained, Dr. Peter Lehnert, BMW Group’s Vice President of Research and New Technologies Digital Car, commented: “We at the BMW Group are convinced that future technologies such as quantum computing have the potential to make our products more desirable and sustainable” (Winners announced in the BMW Group Quantum Computing Challenge, AWS Quantum Computing Blog, https://aws.amazon.com/blogs/quantum-computing/winners-announced-in-the-bmw-group-quantum-computing-challenge/).
  • Remote working: The year 2020 played a pivotal role in the history of humankind. Due to the advent of COVID-19, humans have discovered that they can work from anywhere in the world. This has given rise to the demand for remote working from management and employees. Since there are some instances where you need higher computing power, remote working might not be feasible at all times. However, with most technologies going online and providing a real-time experience of working in the office environment through virtual and augmented reality and better connectivity, businesses can overcome this particular challenge. At the same time, it lowers the administration costs for management. It also helps in reducing storage costs further, which helps in reducing the unit cost for the company.

In order to perform a business task more efficiently and optimally, the business fraternity has started looking for technological solutions. Digital computing in its current state has helped businesses to achieve more efficiency via automation and augmented intelligence. However, current hardware technology has not been able to solve a few complex tasks, which can be associated with an abundance of data and the limitation of computing memory. The following section highlights the types of problems that can be solved by digital computing, and other problems that have generated the need to look beyond the current computing paradigm.

Current business challenges and limitations of digital technology

Digital computers are powered by integrated circuits (ICs), a technology that reached its peak in the 20th century. According to Moore’s law, the number of transistors powering microchips will double every year. In 2021, IBM announced that it can fit 50 billion transistors into its 2 nm chip technology, which basically allows a chip to fit in a space the size of a fingernail. The presence of a large number of transistors has enabled the classical computer to perform large calculations and complex procedures that help in solving day-to-day problems much faster.

However, due to internal leakages and the miniaturization effect, classical gates (OR and AND gates) have been showcasing the quantum effect. Also, digital computers are traditionally unable to solve NP-hard problems (Figure 1.2). In layman’s language, NP-hard problems are measured by the amount of time it takes to solve a problem based on the complexity and number of variables. An example of this, as discussed previously, is how to choose the optimum route out of the 8,543,811,434,435,330 combinations determined for charging station locations in the San Francisco Bay Area. While it would take years for a classical computer to solve the aforementioned problem, ideally, quantum computers can solve it in 3 seconds.

Figure 1.2 – Classification of NP-hard problems based on level of complexity

Figure 1.2 – Classification of NP-hard problems based on level of complexity

To understand the limitations of classical computers in a better way, imagine that you have to pick a portfolio of 100 penny stocks with a limited budget, and let’s assume that the prices are discrete (for example, tick size on stock markets). Suppose you have to construct a portfolio in polynomial time (p = problems a computer can solve in a reasonable amount of time) and assume that it takes 100 steps (n = no. of inputs) to obtain an optimized portfolio or, in other words, n3 time. Theoretically, digital computers will solve the problem in three hours. This problem was easy to solve, and experts can easily verify the solution since we are dealing with stocks of the same class. Hence, we can confidently say that p-class problems are easy to check and solve. Now, the same problem but with a variation (a portfolio optimization of 100 stocks belonging to different risk classes in a limited time) will take around 300 quintillion years to solve, because although the solution is verifiable in a polynomial (n) timeframe, it is obtained in an exponential (NP) timeframe. This problem is classified as an NP problem. For an analogy, imagine a sudoku or tic-tac-toe problem: this is an NP problem for which it is difficult to obtain the solution (it takes exponential time), but easy to verify in polynomial time.

Following on from the preceding discussion, four types of NP problems that are deemed difficult to be solved by digital computers are as follows:

  • Simulation: Computation simulation is modeling a natural world or physical system into a virtual scenario to understand its outcome and impact in advance. For instance, after the subprime crisis of 2008, financial institutions must run a stress test on their underlying assets and their crossholdings to predict the scenario in which the next financial crash could occur. According to one estimate, assessing the probability of a financial crash for a simple network of 20-30 institutions, having exposure in equity, derivatives, fixed income securities, and risk exposure to each other, would take 13.7 billion years, as calculated by a digital computer. This is the estimated time of running a simulation problem that is deterministic in nature and hints at solving a problem’s complexity in n steps in p time, which will not work using current digital technology and thus requires an advanced system to give a faster turnaround.
  • Optimization: Optimization refers to improving the efficiency of an existing algorithm to reduce time complexity. Suppose you have to build a portfolio of 1,000 stocks belonging to 10 sectors. Your client, an international hedge fund, will have to generate several scenarios based on market conditions and thus look for an efficient frontier. These scenarios need to be updated in real time, adjusting themselves based on the risk tolerance limit defined for the portfolio. The classical computer may be able to solve the puzzle using parallel computing, but this might not be the most cost-effective and time-effective strategy. This problem underlies a need for an efficient computer to solve the puzzle in real time.
  • Pattern recognition: The pattern recognition method uses underlying data to discover hidden patterns and trends using machine learning algorithms. However, recent advances in GPU and related technology have enabled programmers to meet with decent success in understanding and uncovering hidden patterns in the given data. In financial fraud, however, the complexity of human behavior makes it difficult for machine learning algorithms to understand the patterns. Theoretically, a computer able to comprehend data in real time can help decode the patterns of financial fraud more successfully.
  • Cryptography: Providing a secure channel for customers to do transactions online in this e-connected world is a foremost priority for banks in the 21st century. All over the world, banks use Rivest, Shamir, and Adleman (RSA) technology based on linear factorization. The recent development of computing prowess hints that such encryption can be easily broken using quantum computers.

To summarize, it will suffice to say that with the limitations observed in current technology, it is time to explore new computing paradigms that can help solve the problems faced by the business fraternity at large and help the industry bring in innovations and creativity.

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Key benefits

  • 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

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.

Who is this book 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.

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

Product Details

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Publication date : May 31, 2023
Length: 292 pages
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Language : English
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Table of Contents

15 Chapters
Part 1: Basic Applications of Quantum Computing in Finance Chevron down icon Chevron up icon
Chapter 1: Quantum Computing Paradigm Chevron down icon Chevron up icon
Chapter 2: Quantum Machine Learning Algorithms and Their Ecosystem Chevron down icon Chevron up icon
Chapter 3: Quantum Finance Landscape Chevron down icon Chevron up icon
Part 2: Advanced Applications of Quantum Computing in Finance Chevron down icon Chevron up icon
Chapter 4: Derivative Valuation Chevron down icon Chevron up icon
Chapter 5: Portfolio Management Chevron down icon Chevron up icon
Chapter 6: Credit Risk Analytics Chevron down icon Chevron up icon
Chapter 7: Implementation in Quantum Clouds Chevron down icon Chevron up icon
Part 3: Upcoming Quantum Scenario Chevron down icon Chevron up icon
Chapter 8: Simulators and HPC’s Role in the NISQ Era Chevron down icon Chevron up icon
Chapter 9: NISQ Quantum Hardware Roadmap Chevron down icon Chevron up icon
Chapter 10: Business Implementation Chevron down icon Chevron up icon
Index Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon

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Jürgen Burger Dec 12, 2023
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I thought the book was pretty good and learned a lot from it.
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Bill Wisotsky Jul 19, 2023
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This book was organized in a very logical way taking you through the current financial quantum landscape. The book moves through creating a foundation quantum computing, the various technologies, and the evolution. It then spends time on a high level, discussing some important types of quantum machine learning algorithms, what they are and why they are used. The authors discuss different types of quantum programming and quantum cloud providers with examples. The authors talk about the financial landscape, what kinds of verticals exist, and the problems that could be potenitally addressed by quantum computing. Part 2 deep dives into these financial ML/QML problems and explains what they are, why they are important and how quantum could help. For example, the authors discuss in detail what portfolio optimization is and gives examples on how to solve it classically. They then discuss how it could benefit from using quantum and then give one or two examples of how to solve it using quantum. What is great is that multiple technologies are shown for the sample problem, (e.g. D-Wave, Qiskit, Circ, PennyLane, etc.). The authors then spend time talking in detail about quantum clouds and QaaS, with detailed explanations and screenshots from different quantum cloud providers. Finally, there is talk about the importance of simulators, noise and the possible future directions of quantum.All-in-all this book is a great addition to anyone needing to understand applied QML in finance and how various technologies and vendors fit into the field and how they could be used to for QML. The book did not have overcomplicated math and went comfortably deep enough that you could try things on your own, but don't need a PhD in physics or mathematics.Highly recommend.
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ML Enthusiast Jun 19, 2023
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The book "Financial Modeling using Quantum Computing" is not an introduction to Quantum Computing. There are alreadymany of those on the market - so there is not necessarily a need for another one. It is a book about APPLICATIONS ofQuantum Computing in financial modeling and decision making. And there is no good presentation about this yet- so it is the right book at the right time!In this book, no mathematical or physical basics are taught in the first part; instead, the followingquestions are answered: What are the principles of Quantum Computing? What is the idea behind the most important algorithms?Which tools/frameworks/solutions can be used for Quantum Machine Learning in particular?How does Quantum Computing fit into the finance industry landscape?The second part of the book goes into more depth: How can the valuation of derivatives be implemented with Quantum Computing?How portfolio management ? And how can credit risk analysis (i.e. rating) be realized with Quantum Computing and what are theadvantages arise?Finally, the third part is to look at the synthesis of simulators, NISQ and HPC; the NISQ hardware roadmap and the implicationsfor potential applications.The book is particularly suitable for those who have a basic knowledge of the basics of Quantum Computing and are looking forits placement in a larger, application-related context. For those, the book is absolutely recommendable!
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Tiny Jul 05, 2023
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Fully implementing the potential for quantum computing as a game-changer and not just another good idea fairy means comprehending how an industry sector can benefit. “Financial Modeling Using Quantum Computing” (Packt, 2023) by Anshul Saxena, PhD, Javier Mancilla Montero, Iraitz Montalban, and Christophe Pere, PhD does exactly that in painstakingly describing which quantum practices can best be translated into a significant ROI (Return on Investment) within the financial sector. The book provides coding examples for several popular quantum languages and describes where, and how, to implement those tools for business. Divided into three sections, the quantum computing paradigm, applications of quantum computing, and upcoming quantum scenarios, this work provides a practical guide to advocate and implement solutions for the business minded. Best recommended for those with a working knowledge of quantum computing and an interest in understanding financial implementations. The Quantum Computing Paradigm starts in the same place as all good quantum books, with a history of quantum computing and what makes it so different than classical methods. Reviews are provided for the most common existing algorithms, available toolsets, and the providers one might use to implement those tools. The last part then provides an overview of financial markets. This covers the various types of financial institutions. One of the most helpful parts was lasting those factors necessary to those institutions in considering new technology - asset management, risk analysis, Investment, profiling, customer identity and retention, Information gaps, customization, and fraud detection. Each area gains substantial benefits from quantum’s ability to handle multiple variables at a greater speed than classical optimization solutions. The middle portion examines derivative valuation, portfolio management and credit risk analytics. Many of these traditional approaches are based on a stochastic analysis, limited only by computing power. Descriptions appear for the Black-Scholes, Monte Carlo, and binomial methods currently in use by financial institutions. Examples with Qiskit and PennyLane provide samples to run one’s own code and examine the differences. Derivatives consider understanding future asset value, portfolio management describes including multiple sectors within those derivatives, and credit risk the potential for whether a bank should risk lending money. Each option can be maximized by understanding a wider variable range which the authors point out best occurs within the quantum annealing sector. Quantum annealing focuses specifically on optimization problems. While the existing coding samples focus on smaller problems, they are easily expandable to handle a real-world implementation. The last chapter here addresses the practical application of using quantum devices attached to a cloud rather than purchasing and maintaining a super-cooled laboratory for one’s own business. The last element considers some practical elements. A helpful definition occurs here in that a simulator is a classical computer running quantum solutions, an emulator introduces the errors when classical code transfers to a quantum implementation, and a device involves an actual solution such as Amazon Braket. The differences allow one to understand the importance of error mitigation within the quantum sphere. One central challenge emerges in the no-cloning theorem when the quantum process cannot be copied without collapsing the quantum state. The other challenge is the relation between physical qubits and logical qubits. Currently, the best solution shows that to receive one logical, error-corrected qubit requires at least 9 physical qubits. This adds perspective when considering the record for the largest quantum computing system is 433 bits, equating to about 40ish logical qubits. Though the book provided exceptional detail, further emphasis on the case studies would have been helpful The coding samples were excellent but the comparison to actual implementation only appeared in 1-2 paragraph segments. Understood that this is a theoretical overview rather than full practical implementation but knowing the existing marketplace solutions can be extremely beneficial. Another useful add would have been a side by side comparison of the different quantum cloud options. Descriptions appear for D-Wave, AWS, IBM, and Azure but a graphic showing the comparative capabilities would have aided greatly. Overall, “Financial Modeling Using Quantum Computing” was an excellent, focused work conveying exactly what the title says. If one is interested in quantum computing in general, it remains useful to understand the various applications. On the other hand, if one works in financial transactions daily, or is charged with improving future applications, this provides an excellent gateway to begin using those tools for oneself. Recommend for financial software developers and those with an interest in quantum computing.
Amazon Verified review Amazon
Aadi Sep 04, 2023
Full star icon Full star icon Full star icon Full star icon Full star icon 5
I liked the connection/flow of the book. It took me 3 months to read this book, was kind of unable to dedicate a lot of time, but it was easy to pick up from the checkpoint. The modelling was necessary for Quantum computing learning newer kids. I learnt some specifics of finances through this book too. If you are in financial domain, give it a try, its a new realm of possibility.
Amazon Verified review Amazon
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