量子ハードウェア

Traditional credit risk assessment models often struggle with accurately capturing complex dependencies, tail risks, and high-dimensional exposure scenarios. A quantum computing proof-of-concept (PoC) in this domain explores how quantum algorithms can enhance modeling capabilities, particularly through more efficient simulation and optimization techniques.

For example, quantum amplitude estimation could accelerate Monte Carlo simulations, potentially reducing the number of samples needed to calculate risk measures like Value-at-Risk (VaR) or Expected Shortfall. Quantum optimization algorithms may also help solve problems such as risk-weighted asset allocation or counterparty exposure minimization more effectively.

Such a PoC typically involves benchmarking quantum approaches against classical baselines, evaluating computational efficiency, and identifying where quantum offers a meaningful advantage. While  early-stage, the exploration helps financial institutions understand how quantum computing may support more adaptive, data-intensive risk models.

Define the payoff, not the circuit
Express multi-asset strikes, correlation structures, and discount factors in Classiq’s Python-like language Qmod. You model the cash-flow logic and probability weights; Classiq handles the quantum circuit implementation.

You pick the algorithm, Classiq optimizes the implementation
The platform maps your description to the right quantum Monte Carlo or Amplitude-Estimation routine, then auto-generates a qubit-efficient circuit: co-optimising depth, qubit count, and error to meet tolerance targets.

Actionable on today’s NISQ hardware
Hardware-aware compilation tailors each circuit to current device noise and connectivity, extracting tighter confidence intervals with fewer samples than classical Monte Carlo, all within the limits of near-term processors.

Future-proof by design
When new QPUs arrive, simply resynthesise. The same high-level model re-compiles for the updated gate set or qubit topology, protecting your analytics stack from hardware churn.