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Paper / 2026SMQ / 101991258278714
A Noise-Aware Quantum Algorithm for Credit Valuation Adjustments on Real Quantum Hardware
Francisco Gómez Casanova / Guillem Borràs Espert / Luis de Pedro Sánchez / Pablo Serrano Molinero / Senaida Hernández Santana
credit riskResource overview

A Noise-Aware Quantum Algorithm for Credit Valuation Adjustments on Real Quantum Hardware

Paper2026

by Francisco Gómez Casanova, Guillem Borràs Espert, Luis de Pedro Sánchez, Pablo Serrano Molinero, Senaida Hernández Santana

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credit risk

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Understand this paper

Suggested foundations for credit and counterparty risk and risk measurement and stress. Specialist prerequisites vary by paper.

Before you read

  1. Credit Instruments, PD/LGD/EAD, and Rating Migration

    Separate default probability, loss severity and exposure.

  2. Conditional Expectation

    Read conditional default and exposure quantities.

  3. Random Variables and Distributions

    Represent losses as a distribution rather than one forecast.

  4. Expected Shortfall and Other Tail Risk Measures

    Distinguish a loss quantile from the average loss in the tail.

Go further

  1. Threshold Models and the Gaussian Copula for Portfolio Default

    Study dependence between defaults.

  2. CVA and DVA

    Connect exposure and counterparty losses to valuation adjustments.

  3. VaR Estimation in Practice: Variance-Covariance, Historical Simulation, Monte Carlo

    Compare risk estimators and their assumptions.

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Explore Counterparty Credit Risk and CVA Explore Quantitative Risk Management

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