  We present and analyze a quantum algorithm to estimate credit risk more
efficiently than Monte Carlo simulations can do on classical computers. More
precisely, we estimate the economic capital requirement, i.e. the difference
between the Value at Risk and the expected value of a given loss distribution.
The economic capital requirement is an important risk metric because it
summarizes the amount of capital required to remain solvent at a given
confidence level. We implement this problem for a realistic loss distribution
and analyze its scaling to a realistic problem size. In particular, we provide
estimates of the total number of required qubits, the expected circuit depth,
and how this translates into an expected runtime under reasonable assumptions
on future fault-tolerant quantum hardware.
