The quantum leap in banking: Redefining financial performance - McKinsey & Company

Quantum computing in finance is emerging as a transformative force with profound implications for the industry. This cutting-edge technology holds the potential to revolutionize how banks operate in three critical areas: optimizing complex financial processes, enhancing the power of machine learning, and strengthening secure communications. Quantum computing is a new approach to calculation that uses principles of fundamental physics to solve extremely complex problems very quickly.1“What is quantum computing?,” McKinsey, March 21, 2025. It excels at solving problems that are currently too intricate or time-consuming for even the most powerful traditional computers. This includes finding the best solutions in scenarios with an overwhelming number of possibilities, extracting deeper insights from vast data sets, and creating fundamentally new ways to protect digital information. While quantum computing in finance is still maturing, leading institutions are actively exploring and demonstrating how these capabilities can deliver significant business advantages, from making more-informed investment decisions to protecting against future cyberthreats. This article examines how banks are already using and advancing quantum computing and offers guidance for others who want to start. Optimization problems involve finding the best possible solution from a vast number of possibilities. Compared with classical methods, which try different paths one at a time, quantum algorithms, such as annealing, can find solutions much faster by using laws of quantum physics.2Quantum annealing uses quantum mechanics to find the optimal solution to complex optimization problems by exploring all possibilities simultaneously to find the lowest-energy state. Tasks such as optimizing portfolios, assessing credit risk, and managing collateral could greatly benefit from quantum computing’s capacity to process complex scenarios quickly. Its applicability to various problems and the magnitude of the advantage is still being researched. Quantum computing in finance is expected to help identify optimal asset allocations significantly faster and more efficiently than conventional computational techniques. This can allow financial institutions to develop more-sophisticated and successful investment strategies by quickly determining the ideal investment mix within a portfolio, enabling faster responses to market fluctuations and a more dynamic approach to managing investment risks and opportunities.

Citi Innovation Labs partnered with Classiq, a quantum computing software company, to explore how quantum computing in finance can improve portfolio optimization. The partnership applied quantum approximate optimization algorithms (QAOAs) to portfolio optimization to see if they offered advantages over classical methods.3QAOA are hybrid quantum–classical algorithms that find approximate solutions to combinatorial optimization problems. They use a variational quantum circuit on a quantum computer, with parameters that are iteratively optimized by a classical computer. They specifically examined how changes to the algorithm’s penalty factor affected its performance. Citi believes this work could lead to improved results for this and other complex challenges in the financial industry.4Louis Thompsett, “Citi explores quantum computing for portfolio optimisation,” Classiq, February 9, 2024. Quantum methods such as quantum Monte Carlo are faster and more efficient than classical approaches. Because quantum computers can process multiple scenarios simultaneously, they can achieve accurate results that would take a classical computer an impractically long time to calculate. They are expected to enable significantly better credit risk models, resulting in more-comprehensive evaluations and more-informed loan offers. They are also more efficient for calculating essential metrics, such as economic capital requirements, which are vital for a bank’s financial stability and strategic planning. The Bank of Canada has researched these methods for bank stress testing. Their studies model the impact of credit shocks and scenarios involving rapid asset sales to see how quantum capabilities could provide computational advantages to improve financial resilience and ensure regulatory compliance.5Vladimir Skavysh et al., “Quantum Monte Carlo for economics: Stress testing and macroeconomic deep learning,” Bank of Canada, June 2022. Quantum computing provides a powerful new way to optimize collateral allocation, a critical task for banks that aims to reduce costs, manage risk, and boost liquidity. Unlike traditional approaches, quantum algorithms can efficiently handle the complex constraints involved in deploying collateralized assets. Multiverse Computing, a quantum software company, has shown this potential in partnership with European financial institutions, including Crédit Agricole CIB and BBVA. They combined tensor networks—a mathematical tool for efficiently representing and manipulating highly complex systems with many variables—with quantum annealing to enhance capital allocation and reduce computation time.6“Quantum computing: Two real-world experiments conducted by Crédit Agricole CIB, in partnership with Pasqal and Multiverse Computing, produce conclusive results in finance,” Multiverse Computing, January 30, 2023; “BBVA and Multiverse use quantum computing to optimize portfolios, improve returns,” Multiverse Computing, August 16, 2021. Quantum machine learning (QML) is expected to detect fraud, predict churn, and generate synthetic data better than classical computing or AI and machine learning. Quantum computing can significantly enhance fraud detection through QML, which enables the rapid and precise analysis of large, complex data sets of transaction data. By improving the accuracy and speed of identifying subtle patterns and anomalies, banks can detect fraud earlier and more accurately, reducing financial losses and enhancing security for institutions and customers. Intesa Sanpaolo, a major Italian banking group, is collaborating with IBM to explore QML for improving the accuracy and speed of fraud detection.7Kate Whiting, “Quantum leaps: 3 ways banks can harness next-gen technologies for financial services,” World Economic Forum, updated July 29, 2025. The bank is using a QML algorithm that can classify and identify patterns in data that are too complex for traditional methods. In their initial tests, the quantum model was able to identify fraudulent transactions with greater accuracy and efficiency, reducing the number of legitimate transactions flagged as false positives and negatives. Quantum computing offers a powerful new capability for predicting churn. By leveraging quantum algorithms, institutions can more accurately identify which customers are at risk of leaving and gain deeper insights into the underlying reasons. This enhanced predictive power enables banks to develop and implement more-effective customer retention strategies, thereby improving loyalty and safeguarding revenue. An example of this application is Itau Unibanco’s collaboration with QCWare, which demonstrated the use of quantum-inspired algorithms to improve financial forecasting, with the specific goal of reducing customer churn. Applied to a data set of approximately 180,000 anonymized customer data points, the quantum-inspired model improved overall precision to 77.5 percent, from 71.0 percent, and increased the number of captured customer withdrawals by 2.0 percent.8Aaron Raj, “QC Ware applies quantum computing principles in banking sector,” TechHQ, May 6, 2022. Quantum computing researchers are investigating methods to create synthetic financial data sets that mirror real data patterns. They aim to lessen reliance on actual data in machine learning and safeguard sensitive customer information. While these techniques are still in early development and mostly tested on small problems, they promise to make machine learning in finance quicker and more precise.

The Fidelity Center for Applied Technology collaborated with IonQ to develop and train sophisticated quantum models capable of producing realistic synthetic financial data.9Sonika Johri and Elton Zhu, “Generative quantum machine learning for finance,” IonQ, updated January 8, 2025. These models accurately reflect complex market behaviors and intervariable relationships, producing more realistic and accurate synthetic financial data than traditional methods. This advancement enables improved testing and validation of financial models, helping institutions improve portfolio management, risk assessment, and trading strategies. Quantum computing presents significant risks as well as opportunities. The immense computational power of future quantum computers poses a direct threat to cryptographic systems that currently secure digital communication and financial transactions. However, it also enables the development of post-quantum cryptography (PQC) and quantum key distribution (QKD) to protect information. Current public-key cryptography relies on mathematical problems that would be easily solvable by a sufficiently powerful quantum computer. PQC uses a new class of mathematical problems that are sufficiently complex to defeat the computational advantages of quantum systems. For financial institutions, therefore, it will be crucial to adopt PQC to maintain the integrity and confidentiality of digital communications. While PQC comprises cryptographic algorithms that run on classical computers, QKD is a hardware-based solution. It uses the laws of quantum physics to enable a verifiably secure exchange of cryptographic keys and can detect any attempt to eavesdrop. It provides a level of protection that is theoretically resistant to future attacks from quantum computers, making it essential for safeguarding highly sensitive financial information and critical infrastructure. PQC and QKD could be used together to create a multilayered defense against future cyberthreats. As part of OpenQKD, an EU-funded initiative to build a quantum communications infrastructure across Europe, financial institutions are piloting QKD to secure critical data links. Danske Bank in Denmark has successfully completed a live QKD-protected transfer between simulated data centers, representing the first quantum-safe data exchange in the Nordics outside a lab environment.10“Danske Bank helps researchers reach milestone within it-security,” Danske Bank, February 23, 2022. Meanwhile, Mt Pelerin, a Swiss crypto-focused institution, has used QKD to test ultrasecure digital asset custody under real-world banking conditions. These pilots—advanced by institutions such as ID Quantique (a Swiss company specializing in quantum cryptography) and DTU (the Technical University of Denmark), which provides research expertise—demonstrate QKD’s potential to future-proof financial communications.11“Mt Pelerin partners with ID Quantique,” Mt Pelerin, June 4, 2019; Ulla Johanne Johansson, “100 kilometres of quantum-encrypted transfer,” DTU, April 3, 2024. Also, HSBC has partnered with Quantinuum to explore how quantum computing can enhance the security of digital assets and distributed ledger systems. HSBC is testing quantum-generated cryptographic keys to secure tokenized gold transactions on its Orion blockchain platform.12“HSBC pilots quantum-safe technology for tokenised gold,” HSBC, September 19, 2024. This works like a lock that changes its combination randomly and continuously, making it far harder for attackers to break in, even with future quantum computers. Early results indicate that quantum-safe encryption can be seamlessly integrated into existing blockchain systems without disrupting them, thereby offering a practical path to protect digital assets against future cyberthreats. Quantum money is a revolutionary concept that allows quantum systems to create unforgeable digital currencies, enhancing financial security and preventing counterfeiting. Unlike traditional currencies, it uses quantum mechanics to embed security features that render replication or forgery physically impossible, providing an unprecedented level of trust in digital transactions. A cutting-edge example of this emerging capability was a joint demonstration by Mitsui, NEC, and Quantinuum of the transmission of unforgeable quantum tokens, a practical version of quantum money, across a ten-kilometer fiber-optic QKD network in Japan.13“Quantinuum together with Mitsui advance unforgeable quantum tokens over fibre network in first ever trial,” Quantinuum, November 18, 2024. McKinsey estimates that the potential economic value from quantum computing in the finance industry is between $400 billion and $600 billion by 2035.14Quantum Technology Monitor, McKinsey Digital, June 2025. Financial services companies are rapidly increasing their exploration of and strategic investments in quantum computing. Leading research firms project a sharp rise in spending on quantum capabilities, with some predicting more than 200-fold growth from 2022 to 2032, at a CAGR of 72 percent.15“The impact of quantum computing on financial services: What to expect?,” Adria, January 17, 2025. This trend is driven by the understanding that quantum computing can fundamentally transform how financial institutions manage risk, optimize operations, detect fraud, and secure vital data. The early applications, often using hybrid quantum–classical methods, are already showing clear benefits in specific cases. Although fully fault-tolerant quantum computers are still years away, industry leaders are increasingly aware that the quantum era is an emerging reality expected to bring transformative results over the next decade or so, making it crucial for leaders to give their attention to it now. Financial institutions can take steps today to prepare for the quantum era. By acting now, they can establish a competitive edge, mitigate future risks, and realize near-term value. Here’s how banks can position themselves effectively for quantum computing: Banks need a plan to guide their quantum efforts. Ideally, it would cover the next two to three years, encompassing immediate opportunities and long-term transformation. Banks don’t need to navigate the quantum journey alone. By partnering with key quantum computing players, they can accelerate their learning curve and gain access to cutting-edge technologies. Quantum computing is a complex and rapidly evolving field. To prepare for its adoption, banks must invest in education and skill development. One of the most immediate challenges posed by quantum computing is its potential to break current cryptographic systems. Banks must act now to safeguard their data and systems against future quantum threats. Quantum computing is still in its early stages, and banks should approach it with a focus on experimentation and learning. Quantum computing represents a paradigm shift for the financial industry, offering transformative potential in areas such as risk management, operational efficiency, and cybersecurity. While the technology is still maturing, the time to act is now. By developing a clear strategy, collaborating with the quantum ecosystem, building internal expertise, preparing for post-quantum security, and starting small to scale strategically, banks can position themselves to thrive in the quantum era. Those that take proactive steps today will be best equipped to lead the next wave of financial innovation. Henning Soller is a partner in McKinsey’s Frankfurt office, Anna Heid is an associate partner in the Zurich office, and Scarlett Gao is an alumna of the London office. Never miss an insight. We'll email you when new articles are published on this topic. These cookies allow us to count visits and traffic sources so we can measure and improve the performance of our site and app. They help us to know which pages are the most and least popular and see how visitors move around the site and app. All information these cookies collect is aggregated and therefore anonymous. 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