Quantum computing in the financial sector: fundamentals, potential and challenges

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Quantum computing is seen as a potential revolution in the financial services sector: it promises to handle complex analyses with a level of performance that is not available to traditional computers (Dietz et al., 2020; Bova et al., 2023). Potential fields of application for quantum computing in banking are diverse and include, for example, portfolio management, risk analysis, corporate finance or customer-oriented analyses (Dietz et al., 2020). Financial institutions can benefit from more effective analysis of large and unstructured data volumes or significantly faster Monte Carlo simulations for risk analyses (Dietz et al., 2020; Business Wire, 2021). In addition, quantum computing can support banks in making informed decisions through comprehensive risk analyses and improve customer service, for example by providing individualized offers such as personally tailored real estate financing (Dietz et al., 2020).

Figure 1: Value of quantum computing use cases by business area in billions of US dollars (Gschwendtner et al., 2023)

Consulting firms estimate the potential benefits in the financial services sector to be worth billions: McKinsey predicts that deployment scenarios could create around USD 622 billion in value by 2035 (cf. Figure 1) (Gschwendtner et al., 2023). BCG estimates up to USD 70 billion in additional operating profit for banks over the next few decades (Bobier, 2020). In view of these prospects, anyone who ignores quantum computing today risks being left behind in the long term. This blog post explains the basics of quantum computing, highlights its diverse potential and fields of application in the financial services sector and discusses the associated technological, security-related and personnel challenges.

Basics of quantum computing

Conventional computers (i.e. computer chips or semiconductors) always deliver the same exact results for the same inputs, making them deterministic. Quantum computers, on the other hand, work probabilistically, delivering results based on probabilities (Dietz et al., 2018; Shipilov, 2019). “Superposition” and “entanglement” are the key mechanisms for the enormous computational “speed-up” of quantum algorithms (Dietz et al., 2020). Quantum computers work with qubits (“quantum bits”) which, unlike classical bits in contemporary computer chips, can simultaneously assume the state 0 and 1, which is known as “superposition” (Shipilov, 2019; Dietz et al., 2020). A system of N qubits can theoretically represent two to the power of N states in parallel, which means exponentially more computing capacity than with N classical bits (Dietz et al., 2020). For example, 4 qubits can simultaneously encode 16 states (Shipilov, 2019), 300 qubits can even represent more possible states than there are atoms in the universe (Dietz et al., 2020). In addition, qubits can be “entangled” with each other (Shipilov, 2019). Entanglement(“quantum entanglement”) refers to a situation in which two or more qubits are connected so that the state of one qubit instantaneously affects the state of the other, regardless of the distance between them (Dietz et al., 2020). Entanglement affects the processing speed of quantum computers because it allows many qubits to be correlated (Dietz et al., 2020). This enables quantum computers to perform operations on multiple qubits simultaneously, which significantly increases their computing power (Quantum Zeitgeist, 2024). A key theoretical example that illustrates both the enormous potential and the considerable risks of quantum computing is Shor’s algorithm from 1994 (Shor, 1994). This algorithm shows that quantum computers can factorize very large numbers efficiently – a task that is practically unsolvable for classical computers. Since many of today’s encryption methods are based on the difficulty of factorizing large numbers, a powerful quantum computer could break these security mechanisms (Shor, 1994; Ruane et al., 2022). Shor’s algorithm thus forms the basis for the fear that many of the encryption methods currently in use may no longer be secure in the future (Auer et al., 2024). This is already seen as an enormous security risk, as sensitive data is already being recorded today to decrypt it later using more powerful quantum machines (Auer et al., 2024, Soutar et al., 2023). One example of this is the so-called “harvest now, decrypt later” attack: attackers are stealing encrypted data today and storing it in the hope of being able to decrypt it in the future using powerful quantum computers (Noone, 2023).

Potential and fields of application

The financial services sector offers many fields of application in which quantum algorithms can bring significant benefits. One particularly important field of application is portfolio optimization (Gschwendtner et al., 2023; FM Contributors, 2023). This involves structuring the distribution of investments in such a way that a specific target, such as maximum return in relation to risk (Sharpe ratio), is achieved (Albareti et al., 2022). While the problem of portfolio optimization is easy to solve in its simplest form, as it can be reduced to a linear system of equations, the calculation becomes significantly more challenging with increasing complexity and additional realistic constraints (Albareti et al., 2022).

Quantum technology can also play a role in risk management. A Monte Carlo simulation based on quantum technology could drastically accelerate risk simulations, which would allow a much faster response to market changes (Orús et al., 2019). Similarly, the complex valuation of derivatives and credit risk management benefit: complicated derivatives and their pricing could be calculated in fractions of the conventional time (Business Wire, 2021; Orús et al., 2019). In credit risk management, the potential of quantum computers is demonstrated, for example, by the fact that the Spanish CaixaBank was able to classify credit risk profiles significantly faster and more efficiently using a quantum algorithm than with traditional methods (Retail Banker International, 2020).

Experts expect both opportunities and threats in cybersecurity and cryptography. Quantum computers can break existing encryption (e.g. RSA, ECC) (Auer et al., 2024, Soutar et al., 2023). This is forcing companies to prepare for “post-quantum cryptography” (PQC) and to invest in quantum key distribution, for example, for long-term data security. At the same time, quantum methods open up new possibilities for security: for example, quantum algorithms could be used to improve the detection of fraud and anomalies (e.g. money laundering) by analyzing huge amounts of transaction data in real time (cf. Chen et al., 2023).

In addition to these main fields of application, there are other potential use cases, such as accelerating liquidity simulations in treasury or specialized quantum-based blockchain and smart contract solutions to improve security (Gschwendtner et al., 2023).

Examples from practice

Large financial institutions are already making concrete preparations. Goldman Sachs, for example, has been working with the startup QC Ware and the quantum manufacturer IonQ for several years (Business Wire, 2021). In September 2021, the partners published a feasibility study in which they showed that today’s quantum hardware can simulate Monte Carlo algorithms for pricing and risk (Business Wire, 2021). JPMorgan Chase is considered a frontrunner in quant engagement according to industry analysis. In 2025, Finextra reports that JPMorgan accounts for two-thirds of all quant job openings in 50 major banks analyzed and over half of quant-related publications in the industry (Finextra Research, 2025). JPMorgan has also set up its own quantum-secure communication network between data centers to protect itself against future threats today (JPMorgan, 2023). UBS is also investing in quantum research collaborations (e.g. with CERN and the Swiss government) to accelerate complex risk simulations and sustainability efforts (UBS, 2023). UBS analysts estimate the total market for quantum computing at USD 300 to 400 billion by 2030 (Boughedda, 2025). Overall, these examples illustrate that large banks are deliberately building teams and partnerships to secure a competitive advantage (Finextra Research, 2025).

Challenges

However, the leap to practical quantum advantage is associated with considerable hurdles. The focus is on technology and scaling up the qubits for increasingly complex calculations. In 2025, quantum computing technology advanced considerably. While early systems had 50 to 100 qubits, also referred to as NISQ or Noisy Intermediate-Scale Quantum (Preskill, 2018), more recent developments in quantum computing have achieved significantly higher qubit numbers, such as IBM’s 1,000-qubit quantum chip (cf. Castelvecchi., 2023). With increasing computing capacity, potential error rates increase. Even tiny disturbances can cause quantum calculations to crash (Dietz et al., 2020). Efforts to minimize these are already ongoing (see Google Quantum AI and Collaborators, 2025). UBS and other experts emphasize that qubit stability and error rates currently inhibit scalability (Boughedda, 2025).

A second major risk factor is cyber security. Experts warn that financial institutions need to act against potential “harvest and decrypt” attacks. The threat of quantum crypto attacks requires urgent preparation, such as the development of quantum-safe crypto procedures, as a successful attack on a payment system could have a global impact in the trillions (Soutar et al., 2023). Therefore, Banks must simultaneously invest in quantum algorithms for attack detection and robustly reorganize their data security.

Thirdly, there is a lack of specialists and expertise. The demand for quantum computer scientists, physicists and mathematicians is growing rapidly, but the supply is barely keeping pace. McKinsey noted in 2022 that investment in quantum start-ups is exploding, while the pool of qualified experts is lagging behind (Masiowski et al., 2022). Large institutions are already competing for quant talent. Professionals with mixed finance and IT skills are in high demand. This shortage delays the development of practical applications and requires expensive build-up programs.

There are also organizational and regulatory challenges: Financial companies must adapt their IT architectures and clarify compliance issues when confidential algorithms run in the quantum cloud. The issue of standardization for quantum algorithms and interfaces is also still unresolved. All of these aspects need to be addressed in parallel with research and pilot projects to ensure a smooth later.

Quantum computing opens up great opportunities for the financial services sector to perform a wide range of tasks faster and more efficiently – be it complex calculations, decision-making or new technologies. At the same time, there are still technical, security and personnel challenges that need to be solved to realize its full potential. However, innovations in the quantum field are continuously progressing, leading to improved qubit stabilities, lower error rates and more efficient algorithms, enabling new, practical applications.

References

Auer, R., Dupont, A., Gambacorta, L., Park, J. S., Takahashi, K., & Valko, A. (2024). Quantum computing and the financial system: Opportunities and risks (BIS Papers No. 149). Bank for International Settlements. https://www.bis.org/publ/bppdf/bispap149.pdf.

Bobier, J.-F., Binefa, J.-M., Langione, M., & Kumar, A. (2020). It’s time for financial institutions to place their quantum bets. Boston Consulting Group. https://www.bcg.com/publications/2020/how-financial-institutions-can-utilize-quantum-computing

Boughedda, S. (2025). Quantum computing to hit a market cap of $300-400B by 2030: UBS. Investing.com. https://www.investing.com/news/stock-market-news/quantum-computing-to-hit-a-market-cap-of-300400b-by-2030-ubs-3824252

Bova, F., Goldfarb, A., & Melko, R. (2023). The business case for quantum computing. MIT Sloan Management Review. https://sloanreview.mit.edu/article/the-business-case-for-quantum-computing/.

Business Wire. (2021). Goldman Sachs, QC Ware and IonQ demonstrate quantum algorithms proof-of-concept that could revolutionize financial services, other industries. https://www.businesswire.com/news/home/20210921005441/en/Goldman-Sachs-QC-Ware-and-IonQ-Demonstrate-Quantum-Algorithms-Proof-of-Concept-That-Could-Revolutionize-Financial-Services-Other-Industries.

Castelvecchi, D. (2023). IBM presents first quantum chip with over 1,000 qubits. Nature. https://doi.org/10.1038/d41586-023-03854-1

Dietz, M., Henke, N., Moon, Backes, J., J., Pautasso, L., & Sadeque, Z. (2020). How quantum computing could change financial services. McKinsey & Company. https://www.mckinsey.com/industries/financial-services/our-insights/how-quantum-computing-could-change-financial-services.

Finextra Research. (2025). JPMorgan leads quantum computing arms race. https://www.finextra.com/newsarticle/45688/jpmorgan-leads-quantum-computing-arms-race

FM Contributors. (2023). Harnessing quantum computing for financial analysis and risk management. Finance Magnates. https://www.financemagnates.com/fintech/data/harnessing-quantum-computing-for-financial-analysis-and-risk-management/

Goldman Sachs, QC Ware & IonQ. (2021). Goldman Sachs, QC Ware and IonQ demonstrate proof-of-concept for quantum algorithms that could revolutionize financial services. Business Wire. https://www.businesswire.com/news/home/20210921005441/en/Goldman-Sachs-QC-Ware-and-IonQ-Demonstrate-Quantum-Algorithms-Proof-of-Concept-That-Could-Revolutionize-Financial-Services-Other-Industries.

Google Quantum AI and Collaborators (2025). Quantum error correction below the surface code threshold. Nature 638, 920-926. https://doi.org/10.1038/s41586-024-08449-y

Gschwendtner, M., Morgan, N., & Soller, H. (2023). Quantum technology use cases as fuel for value in finance. McKinsey & Company. https://www.mckinsey.com/capabilities/mckinsey-digital/our-insights/tech-forward/quantum-technology-use-cases-as-fuel-for-value-in-finance.

JPMorgan (2023). Firm establishes quantum-secured crypto-agile network. JPMorgan. https://www.jpmorgan.com/technology/news/firm-establishes-quantum-secured-crypto-agile-network.

Masiowski, M., Mohr, N., Soller, H., & Zesko, M. (2022). Quantum computing funding remains strong, but talent gap raises concern. McKinsey & Company. https://www.mckinsey.com/capabilities/mckinsey-digital/our-insights/quantum-computing-funding-remains-strong-but-talent-gap-raises-concern.

Noone, G. (2023). Are harvest now, decrypt later cyberattacks actually happening? Tech Monitor. https://www.techmonitor.ai/hardware/quantum/harvest-now-decrypt-later-cyberattack-quantum-computer.

Orús, R., Mugel, S., & Lizaso, E. (2019). Quantum computing for finance: Overview and prospects. Reviews in Physics, 4, 100028. https://doi.org/10.1016/j.revip.2019.100028

Preskill, J. (2018). Quantum computing in the NISQ era and beyond. Quantum, 2, 79. https://doi.org/10.22331/q-2018-08-06-79

Quantum Zeitgeist. (2024). Quantum computing basics: Understanding qubits and superposition. Quantum Zeitgeist. https://quantumzeitgeist.com/quantum-computing-basics-understanding-qubits-and-superposition/.

Retail Banker International. (2020, November 16). CaixaBank uses quantum computing to classify credit risk profiles. https://www.retailbankerinternational.com/news/caixabank-uses-quantum-computing-to-classify-credit-risk-profiles/.

Ruane, J., McAfee, A., & Oliver, W. D. (2022). Quantum computing for business leaders. Harvard Business Review. https://hbr.org/2022/01/quantum-computing-for-business-leaders

Shipilov, A. (2019). The real business case for quantum computing. INSEAD Knowledge. https://knowledge.insead.edu/strategy/real-business-case-quantum-computing.

Shor, P. W. (1994). Algorithms for quantum computation: Discrete logarithms and factoring. In Proceedings 35th Annual Symposium on Foundations of Computer Science (pp. 124-134). IEEE. https://doi.org/10.1109/SFCS.1994.365700

Soutar, B., Dannemiller, D., Buchholz, S., & Bhuta, M. (2023). Quantum computing in financial services. Deloitte Insights. https://www2.deloitte.com/us/en/insights/industry/financial-services/financial-services-industry-predictions/2023/quantum-computing-in-finance.html.

UBS. (2023). UBS cooperates with CERN, EDA and GESDA to promote quantum computing to achieve the UN Sustainable Development Goals. https://www.ubs.com/global/de/media/display-page-ndp/de-20231013-oqi.htmlSpain+3Spain+3Spain+3.

Benjamin Schaefer
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