| Job ID | Type | Status | Objective value | Created | Download |
|---|---|---|---|---|---|
| Portfolio rebalancing | Completed | -14.2 | Today, 10:23 | · | |
| Credit risk diversification | Running | — | Today, 09:15 | — | |
| Reinsurance pool | Completed | -31.5 | Yesterday, 14:02 | · | |
| Minimum variance portfolio | Failed | — | Jun 12, 2025 |
kryptur_sk_demo1234567890abcdefMinimum‑variance portfolio construction
Discretize asset weights into binary variables, encode cardinality and sector constraints as QUBO penalties, then call /v1/solver/optimize. Kryptur returns the top‑k lowest‑variance portfolios with feasibility flags and one‑click download of CSV, ZIP, and PDF risk reports.
from kryptur import Solver
client = Solver(api_key="kryptur_sk_...")
problem = client.risk.portfolio_qubo(
cov_matrix=sigma,
num_assets=20,
max_assets=12,
sector_limits={"tech": 0.25}
)
result = client.optimize(problem, backend="auto")
print(result.best_solution) # variance: 0.0187, feasible ✓
result.download("risk_report.pdf")Credit risk & capital allocation
Optimize loan portfolios to minimize concentration risk while meeting regulatory capital requirements. Kryptur's API handles QUBO formulation from your covariance data, runs QAOA with error mitigation, and delivers ranked solutions with full auditability — essential for model risk management (SR 11‑7, IFRS 9).
POST /v1/solver/optimize
Authorization: Bearer kryptur_sk_...
Content-Type: application/json
{
"linear": [-0.12, 0.34, -0.05],
"quadratic": [[0,1,-0.02],[2,5,0.18]]
}
✓ Response (top 3 solutions)
Rank 1: var=0.011, feasible ✓ | Rank 2: var=0.013, feasible ✓Reinsurance & catastrophe risk pooling
Select the optimal mix of reinsurance treaties to minimize total risk exposure under Solvency II or IFRS 17 constraints. Kryptur handles binary selection variables, loss distributions, and treaty limits as a single QUBO — giving actuaries quantum‑enhanced treaty structures in hours, not weeks.
from kryptur import Solver
problem = {
"linear": [0.05, 0.03, 0.07, 0.02],
"quadratic": [[0,1,0.01],[2,3,-0.02]]
}
result = Solver(api_key).optimize(problem)
print(result.best_solution) # selects treaties 1 & 3Formatted for stakeholders, includes efficient allocation, risk contribution, and classical comparison.
Raw solution vectors, objective values, and feasibility flags for every job.
Complete package: QUBO, QAOA circuit (OpenQASM), raw bitstrings, and convergence plot.