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

Author

Listed:
  • Guillem Borr`as Espert
  • Francisco G'omez Casanova
  • Luis de Pedro S'anchez
  • Senaida Hern'andez Santana
  • Pablo Serrano Molinero

Abstract

Credit Valuation Adjustment (CVA) requires repeated risk-neutral expectation estimation, making it a natural test bed for quantum amplitude estimation, whose coherent amplification can in principle reduce Monte Carlo sampling cost. Whether this advantage survives realistic financial encoding and noisy hardware remains open. We develop an end-to-end, noise-aware quantum workflow for CVA, covering market calibration, discretisation, oracle construction, hardware execution and error-budget analysis. The model combines a correlated two-asset exposure with discount and default factors, encoded through a QCBM-based joint time-market distribution and controlled payoff rotations. We introduce contrast-aware Bayesian iterative quantum amplitude estimation (CABIQAE), which incorporates experimentally calibrated Grover-contrast loss into Bayesian inference and circuit-depth selection. Hardware-calibrated experiments show that CABIQAE exploits the limited amplification available on current devices more effectively than noise-agnostic alternatives and achieves a much lower classical post-processing runtime than the noise-aware BAE baseline. The analysis further decomposes the total CVA error into statistical, encoding, discretisation and hardware contributions. The full CVA oracle remains limited by circuit depth and discretisation resolution.

Suggested Citation

  • Guillem Borr`as Espert & Francisco G'omez Casanova & Luis de Pedro S'anchez & Senaida Hern'andez Santana & Pablo Serrano Molinero, 2026. "A Noise-Aware Quantum Algorithm for Credit Valuation Adjustments on Real Quantum Hardware," Papers 2607.12990, arXiv.org, revised Aug 2026.
    • Handle: RePEc:arx:papers:2607.12990
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    References listed on IDEAS

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    1. Dong An & Noah Linden & Jin-Peng Liu & Ashley Montanaro & Changpeng Shao & Jiasu Wang, 2020. "Quantum-accelerated multilevel Monte Carlo methods for stochastic differential equations in mathematical finance," Papers 2012.06283, arXiv.org, revised Jun 2021.
    2. J. Michael Harrison & Stanley R. Pliska, 1981. "Martingales and Stochastic Integrals in the Theory of Continous Trading," Discussion Papers 454, Northwestern University, Center for Mathematical Studies in Economics and Management Science.
    3. Harrison, J. Michael & Pliska, Stanley R., 1981. "Martingales and stochastic integrals in the theory of continuous trading," Stochastic Processes and their Applications, Elsevier, vol. 11(3), pages 215-260, August.
    4. Jeong Yu Han & Bin Cheng & Dinh-Long Vu & Patrick Rebentrost, 2026. "Quantum advantage for multi-option portfolio pricing and valuation adjustments," Quantitative Finance, Taylor & Francis Journals, vol. 26(3), pages 467-489, March.
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