Link opens in a new tab
opens in new window
![]()
Perspective
4 minute read
Quantum Computing in Financial Services
Six use case classes to prioritize
Quantum computing (QC) is progressing rapidly and financial services firms are exploring where it could deliver advantage—especially across optimization, machine learning, and simulation problem types. This report provides an accessible view of QC applications in financial services and prioritizes use cases by impact and feasibility.
Key takeaways:
- Financial services is rich in use cases. However, not all “quantum-worthy” problems are equal—prioritization matters. The report evaluates more than 50 use cases, consolidates them into approximately 12 classes and uses a weighted business-plus-technical lens to help leaders identify where to focus first.
- Six use case classes rise to the top. The highest-ranked opportunities (by combined business impact and technical feasibility) are derivative pricing, liquidity optimization, portfolio optimization, risk analysis, supervised anomaly detection, and unsupervised anomaly detection.
- Quantum value maps to three “problem families.” The report frames QC applicability around combinatorial optimization, machine learning (for example, classification/anomaly detection) and simulations (for example, Monte Carlo-style modeling).
- A pragmatic reality check: Production workloads are still challenging today. The report emphasizes that many production workloads remain outside current hardware capability, with progress tied to error correction, “logical qubits” and ultimately fault-tolerant quantum computing (FTQC).
- The “best” targets will evolve. Because quantum algorithms and hardware are fast-moving, the report flags that future research may change the relative attractiveness of specific use cases over time.
What’s inside the report
- A concise overview of quantum computing in financial services and why it matters now
- A prioritized view across approximately 12 use case classes and a clear “starting set” of six
- High-level context on constraints (for example, noisy intermediate-scale quantum (NISQ) limitations, the path to logical qubits/FTQC)
How quantum computing use cases were ranked
To rank use cases, the report applies an analytical approach combining quantitative and qualitative inputs, grouping criteria into:
- Business factors such as potential profitability improvement, industry size and degree of regulation.
- Technical factors such as scalability of classical alternatives, complexity of running on quantum hardware and likelihood of speedup.
Each criterion is weighted and use cases are scored. The top six are selected for deeper analysis.
The six classes of use cases with highest business impact
- Derivative pricing: Simulation to enhance pricing of derivatives, options and collateralized debt obligations (CDOs) through value at risk (VaR) calculations.
- Liquidity optimization: Optimization solution that seeks to find the optimal order for transaction processing to maximize the liquidity efficiency for a payment system.
- Portfolio optimization: Optimal solution seeking to maximize return while minimizing risk and considering additional factors.
- Risk analysis: Simulation designed to enhance risk analysis of instruments and counterparty credit risk through VaR.
- Supervised anomaly detection: Machine learning classification task that is trained on labeled cases of fraud/financial anomalies.
- Unsupervised anomaly detection: Machine learning algorithm that does not have labeled training data but learns to identify anomalies.
