Low-degree multilinear angle maps enable O(ε^{-1} log(1/ε)) quantum gate complexity for numerical integration on [0,1], with unconditional separations from classical quadrature for certain low-regularity functions.
Credit Risk Analysis using Quantum Computers
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abstract
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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UNVERDICTED 2representative citing papers
Quantum amplitude estimation algorithm for credit risk economic capital with qubit and runtime estimates on assumed future hardware.
citing papers explorer
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On the complexity of quantum numerical integration: an angle-structure characterization
Low-degree multilinear angle maps enable O(ε^{-1} log(1/ε)) quantum gate complexity for numerical integration on [0,1], with unconditional separations from classical quadrature for certain low-regularity functions.
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Credit Risk Analysis using Quantum Computers
Quantum amplitude estimation algorithm for credit risk economic capital with qubit and runtime estimates on assumed future hardware.