期刊
IEEE TRANSACTIONS ON COMPUTERS
卷 70, 期 12, 页码 2136-2145出版社
IEEE COMPUTER SOC
DOI: 10.1109/TC.2020.3038063
关键词
Qubit; Economics; Computational modeling; Amplitude estimation; Monte Carlo methods; Estimation; Reactive power; Quantum computing; quantum finance; credit risk analysis; economic capital requirement
This study introduces a quantum algorithm for efficiently estimating credit risk, particularly the economic capital requirement. By implementing and analyzing the scaling of the problem to a realistic size, estimates of the total number of required qubits, expected circuit depth, and runtime on future fault-tolerant quantum hardware are provided.
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.
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