> ## Documentation Index
> Fetch the complete documentation index at: https://dynex.mintlify.app/llms.txt
> Use this file to discover all available pages before exploring further.

# Finance

> Quantum computing applications in portfolio optimization, risk management, and financial services

# Financial Services

Quantum computing enables financial institutions to analyze large datasets, optimize asset allocations, and tackle combinatorial problems in risk and fraud management that are intractable classically.

## Portfolio Optimization

Modern portfolio theory requires searching an exponentially large space of asset combinations to find the allocation that maximizes return for a given level of risk. Quantum annealing formulates this as a QUBO problem and finds near-optimal allocations efficiently.

<Card title="Quantum Portfolio Optimization" icon="chart-line" href="https://github.com/Dynex-Development/awesome-dynex/blob/main/optimization/Dynex_Portfolio_Optimisation.ipynb">
  Markowitz mean-variance portfolio optimization on Dynex. Selects optimal asset weights under risk and cardinality constraints.
</Card>

**Scientific background:** Sakuler et al. (2023). *A real world test of Portfolio Optimization with Quantum Annealing.* [DOI:10.21203/rs.3.rs-3959774/v1](https://doi.org/10.21203/rs.3.rs-3959774/v1)

## Collaborative Filtering

Quantum-enhanced recommendation systems and fraud detection via Collaborative Filtering using a Quantum Immune Restricted Boltzmann Machine (CFQIRBM). Models latent user-item interactions as a quantum probabilistic graphical model.

<Card title="Collaborative Filtering (CFQIRBM)" icon="users" href="https://github.com/Dynex-Development/awesome-dynex/blob/main/misc/example_collaborative_filtering_CFQIRBM.ipynb">
  Quantum Immune RBM for collaborative filtering — applicable to fraud pattern detection and personalized recommendations.
</Card>