A quantum computing leap for financial services?

The financial services sector has a deep association with technological change, as an adopter, catalyst and champion, stretching back to the end of the 19th century when transatlantic cables were used to enable electronic funds transfers.

In the last sixty years, we have seen the invention of ATMs, the first digital stock exchange (NASDAQ), SWIFT, and a constant expansion in FinTech, particularly with the development of Open Banking and Open Finance. 

In recent years, alongside AI and distributed ledger technology, quantum computing has emerged as a contender for one of the most transformative technological advances of our time.

We review a recent research paper from the Financial Conduct Authority (FCA) that considers how quantum computing might be applied in UK financial services, some of the key challenges and what firms and regulators can do now to prepare for quantum computing applications.

FCA research

The Financial Conduct Authority is committed to promoting cross-disciplinary research into a range of topics relevant to its role as a regulator, with technology featuring prominently. Back in April 2019, reflecting its goal to be a forward-looking regulator, its research agenda included a focus on technology, big data and artificial intelligence, which highlighted quantum computing as a topic of interest. In 2023, it established its Emerging Technology Research Hub, which monitors technology trends and shares insights into how emerging technologies affect financial services, markets and consumers. This has identified quantum technologies as a key technological trend.

While research notes do not necessarily reflect the FCA's official position, they are part of the evidence that the FCA uses to discharge its functions and inform its views.

A research note published in October 2025 (the Note) considers the potential applications of quantum computing in UK financial services, drawing on contributions from the Quantum Software Lab and the National Quantum Computing Centre, as presenting perspectives from United Kingdom academics, quantum computing companies, financial services firms and UK regulators.

What is quantum computing?

Quantum computers differ from classical computers in the two main characteristics of all computation: information storage and information processing.

While classical computers store memory in binary bits, quantum computers use quantum bits or 'qubits'. In a classical computer, a bit can only exist in one of two states: 0 or 1. By contrast, a qubit can exist in a combination of 0 and 1 states until it is measured (when it resolves into one outcome, either 0 or 1). This ability to hold both probabilities until measured is referred to as the principle of 'superposition'.

A further distinction lies in the relationship between units of information in each form of computing. In classical computing, there is no direct relationship between bits at the hardware level; rather, interactions are programmed through software. However, when two or more qubits are linked they become entangled, forming a direct relationship with each other (this process being referred to as 'entanglement'). Through entanglement, quantum computers can generate complex correlations that are extremely difficult to replicate in classical computing.

Both superposition and entanglement impact how qubits store information and process it. This means that for certain problems, quantum computers are better placed to find solutions efficiently.

Quantum computing and financial services

The interaction between quantum computing and financial services should be understood in the context of the UK government's Industrial Strategy, a ten-year plan to increase business investment significantly in eight growth-driving sectors, one of which is financial services, described at page 8 of the Note as "one of the UK's most productive sectors." To enable growth the UK needs be ready for future technological advances; quantum computing provides new opportunities for solving complex problems, which are not easily solved by classical computing.

To date, significant steps have been taken to build the foundations of a quantum UK ecosystem, in terms of research, infrastructure and investment. The challenge is to translate this into actual impact. The financial services sector is well positioned to support such a transition with its expertise in complicated modelling, optimisation and high-performance computing. Its pro-innovation and competitiveness agenda is also conducive to developing, testing and commercialising quantum computing. As well as driving growth, quantum computing in financial services can strengthen the UK's status as a global financial hub and international leader in financial innovation.

Key potential applications of quantum computing in financial services

As the Note explains, quantum applications for financial services broadly fall into three problem areas (referred to in the Note as "domains"): optimisation, machine learning, and stochastic modelling. Current approaches seek to leverage hybrid quantum-classical methods. The table below outlines the principal use cases for each domain and outlines the advantages and how advanced the use cases are. A more detailed explanation of each domain appears in the subsequent sections.

Optimisation: navigating complex decision spaces

Optimisation problems involve finding the best possible configuration from a set of decisions under given constraints. These problems recur across financial services, from selecting the ideal mix of assets in a portfolio to optimising trading schedules and managing liquidity across the global financial network.

Portfolio optimisation is a core function of investment and asset management, shaping how firms allocate capital, manage risk, and achieve returns. For financial institutions, achieving optimal results translates directly into competitive advantage through improved risk-adjusted performance and efficient capital allocation.

Because, as noted above, unlike a classical bit, a qubit can exist in a state of superposition, quantum computers are able to explore multiple portfolio configurations in parallel. Entanglement enables quantum systems to represent how different assets amplify or offset each other's risk, with diversification effects built into the representation rather than approximated through additional computation. This could deliver two forms of advantage: reduced time to solution and improved solution quality.

Machine learning: enhancing predictive capabilities

Machine learning powers applications from fraud detection and anti-money laundering to portfolio analytics and risk forecasting. Quantum Machine Learning explores whether quantum approaches can overcome classical limitations by using quantum algorithms to process classical datasets.

Quantum approaches encode features into complex quantum states with non-linear, high-dimensional transformations, enabling representation in spaces unachievable for classical methods. In fraud detection, Quantum Support Vector Machines embed transactions into a quantum feature space that is exponentially large in the number of qubits, potentially enabling more effective separation of fraudulent and non-fraudulent transactions.

Stochastic modelling: accelerating Monte Carlo simulations

Stochastic modelling is a type of financial modelling that includes one or more random variables. It estimates how probable outcomes are within a forecast to predict conditions for different situations.

Monte Carlo simulation is a technique that runs stochastic models. It requires running vast numbers of randomised trials to approximate outcomes.

Monte Carlo simulation is frequently used in asset valuation. Asset pricing is a core function in portfolio and risk management, with minor improvements in timing or accuracy able to result in significant returns due to the vast sums of money involved.

Quantum methods to Monte Carlo simulation are often referred to as Quantum Monte Carlo Integration or QMCI and aim to speed up the rate at which Monte Carlo estimates converge. At the heart of QMCI is Quantum Amplitude Estimation (QAE), which is an algorithmic approach that "reframes the estimation process." The error of a Monte Carlo estimate reduces in proportion to the square root of the number of simulations. In classical computing, halving the margin of error requires running four times as many simulations. QAE reduces this to only twice as many.

The impact of quantum computing to Monte Carlo simulations is shown below.

Key challenges in applying quantum computing to financial services

Whilst the potential applications are compelling, significant barriers remain. Near-term commercial applications may be viable, but the full "quantum stack" requires attention. Early applications have been enabled through progress on qubit numbers, stability, and error correction, but the focus must turn to software, algorithms, and integration.

The table below summarises the challenges identified in the Note.

Hardware and error correction

Current quantum computers are restricted by the number of logical qubits they can reliably maintain for computation. Whilst the number of qubits required for portfolio optimisation scales linearly with the number of assets, in practice each logical qubit needs many physical qubits to manage error and noise, with current hardware coming up short by many orders of magnitude.

Even in the most promising demonstrations, error correction overheads are still extremely large. Achieving full fault-tolerance may need "orders of magnitude" more physical qubits to create a single error-corrected qubit of information.

Software, integration, and algorithmic challenges

The majority of today's quantum software packages require developers to write programmes at the circuit level, which approximates to asking classical programmers to write in assembly language. This is not sustainable for large-scale applications.

Integration remains a significant obstacle, as data must move between quantum and classical systems with minimal lag. Without robust bridging software (referred to as "middleware") and workflow automation tools, any theoretical quantum speed-up is soon overshadowed by computational delays, the cost of coordination and capital outlay.

Algorithmic development also presents a major challenge. In the last couple of decades, the number of new algorithms with a clear "quantum advantage" has been relatively few. The number is even smaller when one considers those that have commercial use cases. In the domain of optimisation in financial settings, for example, current quantum algorithms do not perform as well as their classical counterparts, which have been fine-tuned over many years.

Cost and commercial viability

Unsurprisingly, cost remains a critical factor in all three domains. A theoretical quantum improvement is only valuable if it results in concrete benefits that outweigh initial costs. Classical approaches hold a significant head start and continue to evolve, making the bar for quantum adoption high.

What firms, vendors and regulators can do now

Although quantum computing is still largely an area of theoretical exploration, it is becoming a topic of practical consideration, which marks an important crossroads for the UK. The table sets out the key actions and expected outcomes for each of the principal stakeholders (firms, vendors and regulators), which are considered in further detail below.

Actions for financial services firms

Firms should develop quantum readiness strategies that remain effective irrespective of the pace at which, or direction in which, the technology develops. No regret actions include developing quantum skills and engaging in collaborative proofs-of-concept with vendors and academia, which provide essential insights whilst channelling investment and financial expertise into the ecosystem.

Most firms have established relatively small teams to lead preparatory work, focusing on building technical understanding and exploring quantum-inspired approaches that use quantum principles on classical hardware, seeking practical near-term performance improvements while building both knowledge and skills to reap the benefits of quantum in the future.

Actions for quantum computing vendors

Within the quantum computing vendor sector, limited awareness of regulatory frameworks and what the regulators expect risks creating perceived regulatory barriers that discourage experimentation and hold up implementation. Direct engagement with regulators on proofs-of-concept and potential use cases helps to establish mutual understanding, counter misconceptions, and provide clarity on the application of regulatory frameworks, and will ultimately be key to unlocking investment from end-users.

Actions for UK regulators

Build knowledge and establish dialogue

Regulators must build knowledge of quantum computing and develop open channels for dialogue. They need both technical understanding (to anticipate where quantum applications might arise) and trusted relationships across the ecosystem (to ensure effective engagement as applications take shape). The UK Quantum Regulators' Forum, which was a recommendation of the Regulatory Horizons Council (which became part of the Regulatory Innovation Office in October 2025) and was launched in April 2025 with founding members that include the FCA, is an important initiative towards achieving this.

Adapt innovation tools

Existing innovation services, such as sandboxes, could be expanded to give firms safe spaces for experimentation with access to quantum computing resources, integrated development environments and synthetic data for testing. Regulators could also consider direct engagement with proofs-of-concept, staging or attending events and forums, and creating small quantum-focused teams.

Develop an Applications Regulatory Readiness Framework

UK regulators should build on the Proportionate and Adaptive Governance of Innovative Technologies framework (a framework introduced in a report by the Regulatory Horizons Council) by developing an Applications Regulatory Readiness Framework. This would be used to assess the maturity of individual applications and determine appropriate forms of engagement at each stage: from initial dialogue through to pilots and commercialisation. Such an approach would demonstrate that UK regulators are adopting new methods to engage with disruptive new technologies.

Address cross-cutting considerations

There are a number of points of interaction between quantum and the FCA's remit, which will be key to its ability to minimise risk arising out of the application of quantum computing in UK financial services. In particular:

• Explainability is central to fair outcomes and regulatory confidence. Under the Consumer Duty, firms must demonstrate how decisions were reached. Quantum models, like complex AI systems, risk exacerbating the 'black box' challenge.
• Market consolidation in the quantum ecosystem is likely over time, which may lead to risks of concentrations and dependency that have been observed in other technologies including AI. The FCA's operational resilience framework is highly relevant for these potential dependencies.
• Market surveillance will be needed to ensure market integrity, fairness and competition.
• International alignment is essential to avoid fragmentation or regulatory arbitrage. It is essential for engagement in fora such as the International Organization of Securities Commissions and the Financial Stability Board to continue.

Conclusion

There is still some way to go before quantum computing is fully embedded in financial services and the journey towards quantum advantage in financial services is unlikely to be swift or straightforward. Notwithstanding, the foundations are being laid today and we are at a critical juncture. By taking coordinated action now – building skills, promoting proactive engagement and collaboration, along with setting up clear regulatory pathways – the UK can position itself at the forefront of the quantum revolution in financial services.