Modern finance relies on complex mathematical models to price derivatives, optimize portfolios, and manage risk under uncertainty. With ColibriTD’s hybrid quantum-classical platform QUICK, financial institutions can accelerate computations, explore higher-dimensional problems, and prepare for the quantum advantage, all without relying on large datasets.
Exotic financial instruments require solving generalized Black–Scholes PDEs in high-dimensional spaces. H-DES efficiently solves multidimensional PDEs, estimating both price and sensitivities with unprecedented speed and accuracy.
Faster pricing and calibration
Improve precision on volatility surfaces through hybrid simulation
Price sophisticated or complex-geometry derivatives that were previously out of reach.
Asset managers face high-dimensional, constrained, multi-objective optimization problems to maximize risk-ajusted returns, comply with regulations, and account for liquidity, sector exposure, or budget constraints. H-DES identifies Pareto-optimal portfolios, accelerating convergence.
Our hybrid algorithms allow for a wider exploration of the risk-return trade-offs.
Significant reduction in total computation time enabling faster decision-making.
Enhanced risk-adjusted performance of portfolios.
Integrate thousands of variables and constraints into a single optimization pass, breaking the dimensionality curse of classical computation.
Interest rates and currency fluctuations are governed by coupledstochastic differential equations. Our platform solves theseefficiently enabling rapid scenario testing and stress analysis.
Quantum computing is increasingly explored for computational problems that appear in quantitative finance. Many financial models rely on heavy numerical simulations and complex mathematical structures.
Examples of potential quantum use cases include:
These problems often require large computational resources when the number of assets, risk factors, or market scenarios grows.
ColibriTD develops a hybrid quantum algorithms called Hybrid Differential Equation Solver (H-DES) that can be tested on mathematical models similar to those used in quantitative finance. Through a project with ColibriTD, financial institutions can experiment with quantum algorithms on complex numerical models while keeping their existing computing workflows.
Monte Carlo simulations are widely used in finance to model uncertainty and estimate the behavior of financial instruments under many possible scenarios.
These simulations are used for tasks such as derivative pricing, risk estimation, and portfolio analysis. They often require running thousands or millions of scenarios, which can become computationally expensive.
Quantum computing introduces new algorithmic approaches that could reduce the computational cost of certain probabilistic simulations.
ColibriTD develops hybrid quantum algorithms designed to explore complex numerical models. These algorithms can be tested on problems that involve large scale simulations, allowing financial research teams to evaluate how quantum approaches could improve performance in the future.
Portfolio optimization involves selecting asset allocations that maximize expected return while controlling risk and respecting multiple constraints.
As the number of assets increases, the number of possible portfolio configurations grows rapidly. This makes the optimization problem computationally complex.
Quantum computing introduces algorithmic approaches that explore large solution spaces differently from classical optimization methods.
ColibriTD develops hybrid algorithms such as H-DES that combine classical optimization with parameterized quantum circuits. These algorithms allow financial institutions to experiment with quantum approaches for exploring complex optimization problems.
