Focus Finance
Quantum computing in financial markets
Finance was one of the first industries to take quantum computing seriously, because so much of it is computation under uncertainty. Here is where it could help, what it threatens, and what to do now.
01 Opportunity
Where quantum could help
The candidates share one trait: the computation itself is the bottleneck, not the data or the process around it.
Pricing and risk
Monte Carlo simulation underpins derivative pricing and risk measures such as VaR and CVA. A quantum method called amplitude estimation can reach the same accuracy with quadratically fewer samples. That matters most for the heaviest, slowest calculations.
error ∝ 1/N, not 1/√N
Portfolio optimization
Choosing assets under real constraints, such as lot sizes, sector limits and transaction costs, is a hard combinatorial problem. Quantum and quantum-inspired optimizers are being tested on it, mostly in hybrid setups alongside classical solvers.
constrained, combinatorial
Machine learning
Fraud detection, credit scoring and market prediction all run on machine learning. Quantum machine learning may help with some of these, but results so far are small-scale, and strong classical models often close the gap.
early: benchmark carefully
Certified randomness
A quantum computer can produce random numbers that a classical computer can check are freshly generated, assuming the adversary has limited computing power. JPMorgan Chase, Quantinuum and US national labs demonstrated this on a 56-qubit trapped-ion machine, published in Nature in 2025.
demonstrated, not yet production
The honest timeline
Most of the big wins in pricing and optimization need fault-tolerant machines. Algorithms such as amplitude estimation consume large amounts of magic, and today’s hardware can’t supply it at scale yet.
Expect pilots and research now, and production advantage later. The institutions that build skills, data and partnerships today will be the ones ready to use it.
02 In the field
What banks are piloting
Quantum technologies are already being tested in financial services. A 2025 World Economic Forum report, written with Accenture, highlights these cases.
Yapı Kredi
The Turkish bank used D-Wave’s quantum technology to model its network of small and medium-sized business clients and find where one failure could set off a chain reaction. The bank says an analysis that would traditionally take years ran in seven seconds.
risk forecasting
HSBC
Uses post-quantum VPN tunnels and quantum random number generation to protect tokenized gold transactions.
quantum-safe security
Banco Sabadell
Ran a four-month project on adopting post-quantum cryptography, focused on crypto-agility: being able to switch algorithms quickly as threats and standards change.
quantum-safe security
Intesa Sanpaolo
Tested quantum machine learning for fraud detection, running variational quantum classifiers with IBM’s tools on hundreds of thousands of transactions. According to the report, the quantum model was more accurate than traditional methods while using fewer data features.
fraud detection
Why fraud matters
Fraud cost UK banks $1.6 billion in 2024. In April 2025 the UK government committed $162 million to quantum technology for tackling crime, fraud and money laundering.
The report’s message to banks: to get real value, go beyond experiments and pilots, with sustained R&D, infrastructure, partnerships with start-ups and the public sector, and a trained workforce.
Source: World Economic Forum ↗03 Risk
The same machines threaten your cryptography
Public-key cryptography protects payments, trading links, customer logins and digital signatures. A large enough error-corrected quantum computer would break it.
Harvest now, decrypt later
Financial records, client data and long-term keys often have to stay confidential for a decade or more. An attacker can copy encrypted traffic today and read it once a capable machine exists, so for long-lived data the risk has already started.
Quantum-safe security →04 Action
What to do now
None of this needs a quantum computer on site. It needs a plan.
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Pick the right problems
Identify two or three workloads where computation is the real bottleneck. Benchmark the best classical methods first, so any quantum result has an honest baseline.
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Start the quantum-safe migration
Inventory where you use public-key cryptography, and prioritize long-lived data and signing keys. It is the one quantum task with deadlines already proposed by standards bodies.
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Build skills and partners
A small internal team, access to quantum hardware through the cloud, and relationships with vendors and researchers. That is enough to move quickly when the hardware matures.
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Report it upward
Give your board and risk committee a clear view of quantum risk and opportunity, with owners and dates.
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