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Credit Risk Analysis Using Quantum Computers
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DOI：10.1109/TC.2020.3038063.png)
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Abstract
En 中文
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.
Keywords:
Qubit
Economics
Computational modeling
Amplitude estimation
Monte Carlo methods
Estimation
Reactive power
Quantum computing
quantum finance
credit risk analysis
economic capital requirement
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