How is the development of quantum computing being funded?

Globally, investment in quantum technologies is estimated to have reached approximately $33 billion in 2025, of which 38% was specifically allocated to quantum computing, establishing it as the most dynamic segment of the ecosystem.

The role of the public sector, given its strategic importance, is particularly significant: cumulative public funding commitments exceeded $55 billion in April 2025, with China, the EU and Japan as the main drivers, respectively. These investments reflect the high capital requirements, long development horizons and technological uncertainty associated with these technologies, which necessitate the development of quantum ecosystems; consequently, the public sector is playing a significant role in supporting higher-risk investments in the early stages.

For its part, private investment — primarily through venture capital funds, hedge funds, angel investors, accelerators and large corporations — has been key to the development of the ecosystem. After exceeding $2 billion in 2021 and 2022, investment in quantum startups moderated in 2023 and recovered in 2024, against a backdrop of increased competition for capital with other emerging technologies such as artificial intelligence.

From a geographical perspective, the United States leads the global ecosystem in terms of total investment, innovation and number of companies, supported by a market-driven model that promotes collaboration between companies and universities. In contrast, the EU has a solid scientific base, but its companies tend to rely more heavily on public funding due to lower levels of private investment.

Overall, the funding pattern reflects a rapidly expanding sector that is still in its early stages, with high volatility in valuations and a significant reliance on public support, against a backdrop of uncertainty regarding its adoption timeline and cost-reduction potential. Against this backdrop, specialised investment vehicles are beginning to emerge, including some EU-domiciled exchange-traded funds (ETFs) — launched by managers such as iShares, VanEck and WisdomTree — as well as venture capital initiatives such as the fund backed by the Danish firm 55 North.

In line with this trend, venture capital funding has reached high levels globally by 2025. In Europe, there has been a notable increase in such funding, although it remains concentrated in a small number of companies: eight start-ups² have secured investments exceeding €100 million, a further 52 have collectively raised around €1 billion, and one of them has surpassed a valuation of $1 billion. The sector’s significant funding needs highlight the necessity to make progress on the integration of European capital markets, as well as to promote initiatives such as the Savings and Investments Union.

² Four based in the UK, two in France, one in Finland and one in Spain.

What are the applications of quantum computing in finance?

Quantum computing has the potential to transform the financial sector by offering processing and analytical capabilities that far exceed those of classical computing for certain complex problems. Specifically, quantum algorithms enable calculations to be performed with greater efficiency and speed than classical methods — a phenomenon known as quantum speedup —.

Potential applications of quantum computing that have been explored in the financial sector include:

1. Optimisation. Quantum algorithms can improve the resolution of complex optimisation problems, such as those related to portfolio optimisation, risk mitigation and hedging. They can also be useful in index replication or tracking³ and trade settlement⁴. Furthermore, they can be applied to economic and financial modelling, including the prediction of financial contagion in networks of interconnected institutions during periods of stress and the identification of foreign exchange arbitrage opportunities. Finally, it is worth highlighting the possibility of adopting hybrid approaches in which quantum computing is used to identify asset combinations, whilst classical methods carry out the precise allocation within the portfolio.
2. Stochastic modelling. Stochastic modelling uses random processes and Monte Carlo simulations⁵ to model financial variables such as share prices, interest rates and volatility. The use of quantum computing enables these simulation methods to be accelerated in the valuation of financial instruments — including European and exotic options and collateralised debt obligations (CDOs) —. These tools can also improve the calculation of risk metrics, such as value at risk (VaR) and credit risk analysis. Despite the theoretical advantage offered by quantum modelling, numerous challenges remain, such as the encoding of classical probability distributions into quantum states or hardware limitations — such as the low number of qubits and lack of fault tolerance —.
3. Machine learning (ML). The use of quantum ML (QML) algorithms can aid in the detection of complex patterns in large volumes of data, with applications in credit risk assessment, fraud detection and other classification and prediction tasks — such as credit rating downgrades —. QMLs fall into two main categories: (1) methods that accelerate classical ML techniques, whose task is to search for patterns in conventional data by encoding them into quantum states and processing them with basic quantum linear algebra subroutines (QBLAS); and (2) native quantum ML algorithms that operate directly on quantum data generated within a quantum system. Although their applicability and practical relevance need to be reviewed, native approaches are considered more promising, although it is unclear whether they can offer a significant advantage in the financial sector.
4. Decentralised ledger technologies (DLT). Firstly, quantum computing may have a significant impact on DLT-based platforms, such as blockchain, as it could compromise the public-key cryptography used in them, as will be explained in more detail in the following section. Consequently, one of the alternatives being explored is the incorporation of advanced cryptographic techniques, based on quantum computing, in order to enhance their security. Another possible application involves using quantum computers to speed up the mining of bitcoins and other crypto-assets (CA) when performing calculations related to the so-called proof-of-work (PoW) and even to enable quantum PoW mechanisms, which would significantly reduce electricity consumption.
   Furthermore, quantum technology could improve the scalability and speed of decentralised consensus mechanisms, whilst also facilitating interoperability between different blockchains. In turn, it could be applied to the tokenisation of assets and to shared know-your-customer (KYC) processes between different financial institutions.

Although these applications offer significant advantages in terms of computational efficiency and processing, integration with traditional systems and the development of specific algorithms are still at an early experimental stage.

³ In particular, in the selection of assets to replicate a broader market index.

⁴ For example, when a clearing house determines the maximum number of transactions that can be settled between multiple participants without exceeding their credit limits, resolving the complex interdependencies between the parties involved.

⁵ This consists of a numerical simulation technique used to estimate the expected value of a quantity that depends on one or more random variables, involving the generation of a large number of sampling trajectories based on underlying stochastic (random) factors. It is used, above all, for asset and risk valuation, but may require significant computational effort (for example, if the scale of the problem increases), so the use of quantum algorithms would facilitate the calculations.

What are the risks associated with quantum computing?

Despite the numerous opportunities offered by quantum computing in the financial sector, its development poses significant risks that could affect its operation and security.

In particular, the main concern lies in its potential to compromise current cryptographic mechanisms, by solving certain mathematical problems — on which their security is based and which classical computers are unable to solve — much more quickly. The financial system — including banking, payment services, securities markets and asset management — relies heavily on public-key cryptography to perform key functions, such as digital signatures or ensuring the security of transactions, data integrity and the confidentiality of communications. In particular, in the field of DLT, the development of sufficiently advanced quantum computers could enable the breaking of some of these cryptographic systems and facilitate access to the private key from the public key, with a view to subsequently using it without the holder’s consent. Furthermore, the threat has a significant temporal dimension, as information encrypted today could be stored and decrypted in the future, once quantum technology reaches sufficient maturity.

Another risk to be considered is that of concentration within the sector, as the quantum computing market could become concentrated amongst a small number of suppliers. Such centralisation would create critical operational and supply dependencies for financial institutions, industries and public administrations.

Given the systemic and cross-sectoral nature of this challenge, initiatives aimed at facilitating the transition to encryption methods resistant to both classical attacks and those arising from quantum computing (post-quantum cryptography) are being stepped up.

Its gradual adoption is also emerging as a key element in preserving the stability and resilience of the financial system in the long term. Specifically, the EC has established a specific roadmap for the transition to post-quantum cryptography. The main milestones of this plan are to begin planning the transition and launching pilot projects in 2026, to complete the transition for use cases considered high-risk by 2030, and for medium-risk use cases by 2035.

For its part, the Digital Operational Resilience Regulation (DORA)⁶ incorporates preparations for quantum computing into the EU regulatory framework by imposing obligations on financial institutions to implement cybersecurity risk management measures that address cryptographic vulnerabilities arising from technological advances, explicitly mentioning quantum computing.

⁶ Regulation (EU) 2022/2554 of the European Parliament and of the Council, of 14 December 2022, on digital operational resilience in the financial sector and amending Regulations (EC) No 1060/2009, (EU) No 648/2012, (EU) No 600/2014, (EU) No 909/2014 and (EU) 2016/1011.