Today’s Brief Commentary:
This is a special issue of the newsletter in that instead of news articles and research papers, I’ve included several links that give the current state of quantum computing for finance.
If you do a web search on the terms “finance” and “quantum,” you will see many results related to cybersecurity. The finance services industry is a key target for hackers because of the high value of the assets and the extensive amount of personal information. I’m tempted to tell the old joke: “Why do people rob banks? That’s where the money is kept.” Well… I guess I just did.
Security is a significant concern, but this collection is about financial algorithms and use cases. As you look through these, ask yourself these questions:
- How large a quantum computer will we need for these applications?
- Are fault tolerance and logical qubits required to find solutions for non-trivial problems?
- What’s the crossover point where quantum can do a better job than classical in terms of speed, accuracy, cost, resource use, and energy consumption?
Don’t forget to check out and bookmark our new sortable list of upcoming quantum technology conferences.
The latest Sutor Group report is freely available online: Quantum Processing Unit (QPU) Market Landscape (Abridged) – April 3, 2025. Updates and the full report are available for purchase or by subscription. Contact us for details.
Contents
Quantum Computing for Financial Services
Exploring quantum computing use cases for financial services | IBM
Date: Thursday, September 12, 2019
Excerpt: Financial services has a history of successfully applying physics to help solve its thorniest problems. The Black-Scholes-Merton model, for example, uses the concept of Brownian motion to price financial instruments – like European call options – over time.
Applying emerging quantum technology to financial problems—particularly those dealing with uncertainty and constrained optimization—should also prove hugely advantageous for first movers. Imagine being able to make calculations that reveal dynamic arbitrage possibilities that competitors are unable to see. Beyond that, greater compliance, employing behavioral data to enhance customer engagement, and faster reaction to market volatility are some of the specific benefits we expect quantum computing to deliver.
Quantum technology use cases as fuel for value in finance | McKinsey Digital
Date: Monday, October 23, 2023
Excerpt: In this post, we offer an overview of potential quantum computing use cases across business units in financial services: corporate banking, risk and cybersecurity, retail banking, payments, wealth management, investment banking, and operations and finance. Using market analysis and expert interviews, we have identified the possible impact of quantum-technology use cases for each business unit and the value at stake.
Quantum computing and the financial system: opportunities and risks | BIS
https://www.bis.org/publ/bppdf/bispap149.htm
Authors: Raphael Auer; Angela Dupont; Leonardo Gambacorta; Joon Suk Park; Koji Takahashi; and Andras Valko
Date: Friday, October 4, 2024
Excerpt: Quantum computers are still in an experimental phase, but in the future, they may have a profound impact on the financial system. By providing faster and potentially more efficient solutions, quantum computers have the potential to solve certain complex problems that are of paramount interest in the field of economics and finance. For example, quantum simulation algorithms can be leveraged in stress testing and macroeconomic analysis, and quantum optimisation can be used in asset pricing. Meanwhile, the advent of quantum computers also introduces a potential threat to financial stability, especially through their ability to breach some of the most widely used cryptographic algorithms. Despite the nascent state of quantum computing development, the potential for sensitive data to be stored now with the intention to be decrypted later necessitates immediate preparation. This paper explores the transformative potential of quantum mechanics and its applications to the financial system, including the potential benefits as well as the main risks.
The Future of Portfolio Optimization is in Quantum | IQM Quantum Computers
https://meetiqm.com/case-study/the-future-of-portfolio-optimization-is-in-quantum/
Date: Wednesday, January 22, 2025
Excerpt: A recent collaboration between IQM and DATEV demonstrated the potential of better portfolio optimization using a quantum computer. DATEV presented an industry-relevant case, while IQM provided a quantum computing approach, revealing the potential of the technology.
Leverage Quantum Advantages in Finance | Classiq
https://www.classiq.io/industries/industries-finance
Date: Thursday, April 17, 2025
Excerpt: Model while the platform seamlessly converts it into an optimized quantum circuit. This is quantum computing simplified and made accessible, enabling you to stay at the forefront of the financial industry. Quantum computing use cases for finance allows you to optimize trades, risk management, and targeting. Quantum computing applications in finance can enhance portfolio optimization for better client outcomes.
Quantum Computing for Financial Services | Technical
[2201.02773] A Survey of Quantum Computing for Finance
https://arxiv.org/abs/2201.02773
Authors: Herman, Dylan; Googin, Cody; Liu, Xiaoyuan; Galda, Alexey; Safro, Ilya; Sun, Yue; Pistoia, Marco; and Alexeev, Yuri
Date: Saturday, January 8, 2022
Excerpt: Quantum computers are expected to surpass the computational capabilities of classical computers during this decade and have transformative impact on numerous industry sectors, particularly finance. In fact, finance is estimated to be the first industry sector to benefit from quantum computing, not only in the medium and long terms, but even in the short term. This survey paper presents a comprehensive summary of the state of the art of quantum computing for financial applications, with particular emphasis on stochastic modeling, optimization, and machine learning, describing how these solutions, adapted to work on a quantum computer, can potentially help to solve financial problems, such as derivative pricing, risk modeling, portfolio optimization, natural language processing, and fraud detection, more efficiently and accurately. We also discuss the feasibility of these algorithms on near-term quantum computers with various hardware implementations and demonstrate how they relate to a wide range of use cases in finance. We hope this article will not only serve as a reference for academic researchers and industry practitioners but also inspire new ideas for future research.
[2404.10088] Quantum Risk Analysis of Financial Derivatives
https://arxiv.org/abs/2404.10088
Authors: Stamatopoulos, Nikitas; Clader, B. David; Woerner, Stefan; and Zeng, William J.
Date: Monday, April 15, 2024
Excerpt: We introduce two quantum algorithms to compute the Value at Risk (VaR) and Conditional Value at Risk (CVaR) of financial derivatives using quantum computers: the first by applying existing ideas from quantum risk analysis to derivative pricing, and the second based on a novel approach using Quantum Signal Processing (QSP). Previous work in the literature has shown that quantum advantage is possible in the context of individual derivative pricing and that advantage can be leveraged in a straightforward manner in the estimation of the VaR and CVaR. The algorithms we introduce in this work aim to provide an additional advantage by encoding the derivative price over multiple market scenarios in superposition and computing the desired values by applying appropriate transformations to the quantum system. We perform complexity and error analysis of both algorithms, and show that while the two algorithms have the same asymptotic scaling the QSP-based approach requires significantly fewer quantum resources for the same target accuracy. Additionally, by numerically simulating both quantum and classical VaR algorithms, we demonstrate that the quantum algorithm can extract additional advantage from a quantum computer compared to individual derivative pricing. Specifically, we show that under certain conditions VaR estimation can lower the latest published estimates of the logical clock rate required for quantum advantage in derivative pricing by up to $\sim 30$x. In light of these results, we are encouraged that our formulation of derivative pricing in the QSP framework may be further leveraged for quantum advantage in other relevant financial applications, and that quantum computers could be harnessed more efficiently by considering problems in the financial sector at a higher level.
[2407.12618] A Brief Review of Quantum Machine Learning for Financial Services
https://arxiv.org/abs/2407.12618
Authors: Doosti, Mina; Wallden, Petros; Hamill, Conor Brian; Hankache, Robert; Brown, Oliver Thomson; and Heunen, Chris
Date: Wednesday, July 17, 2024
Excerpt: This review paper examines state-of-the-art algorithms and techniques in quantum machine learning with potential applications in finance. We discuss QML techniques in supervised learning tasks, such as Quantum Variational Classifiers, Quantum Kernel Estimation, and Quantum Neural Networks (QNNs), along with quantum generative AI techniques like Quantum Transformers and Quantum Graph Neural Networks (QGNNs). The financial applications considered include risk management, credit scoring, fraud detection, and stock price prediction. We also provide an overview of the challenges, potential, and limitations of QML, both in these specific areas and more broadly across the field. We hope that this can serve as a quick guide for data scientists, professionals in the financial sector, and enthusiasts in this area to understand why quantum computing and QML in particular could be interesting to explore in their field of expertise.
Quantum Computing for Optimization | Technical
[2312.02279] Challenges and Opportunities in Quantum Optimization
https://arxiv.org/abs/2312.02279
Authors: Abbas, Amira; Ambainis, Andris; Augustino, Brandon; Bärtschi, Andreas; Buhrman, Harry; Coffrin, Carleton; Cortiana, Giorgio; Dunjko, Vedran; Egger, Daniel J.; ; …; and Zoufal, Christa
Date: Monday, December 4, 2023
Excerpt: Recent advances in quantum computers are demonstrating the ability to solve problems at a scale beyond brute force classical simulation. As such, a widespread interest in quantum algorithms has developed in many areas, with optimization being one of the most pronounced domains. Across computer science and physics, there are a number of different approaches for major classes of optimization problems, such as combinatorial optimization, convex optimization, non-convex optimization, and stochastic extensions. This work draws on multiple approaches to study quantum optimization. Provably exact versus heuristic settings are first explained using computational complexity theory – highlighting where quantum advantage is possible in each context. Then, the core building blocks for quantum optimization algorithms are outlined to subsequently define prominent problem classes and identify key open questions that, if answered, will advance the field. The effects of scaling relevant problems on noisy quantum devices are also outlined in detail, alongside meaningful benchmarking problems. We underscore the importance of benchmarking by proposing clear metrics to conduct appropriate comparisons with classical optimization techniques. Lastly, we highlight two domains – finance and sustainability – as rich sources of optimization problems that could be used to benchmark, and eventually validate, the potential real-world impact of quantum optimization.
Sutor Group Intelligence and Advisory
Dr. Bob Sutor is the CEO and Founder of Sutor Group Intelligence and Advisory. Sutor Group provides broad market insights and deep technical expertise based on over four decades of experience with startups and large corporations. It advises Deep Tech startups, companies, and investors on quantum technologies, AI, and other emerging tech fields.
Sutor Group shares its knowledge and analysis through direct client engagements and seminars, reports, newsletters, books, written and on-air media appearances, and speaking and panel moderation at the top conferences and client events.
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