A Noise-Aware Quantum Algorithm for Credit Valuation Adjustments on Real Quantum Hardware
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.
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BibTeX
@misc{espert2026noiseaware,
title = {{A Noise-Aware Quantum Algorithm for Credit Valuation Adjustments on Real Quantum Hardware}},
author = {Guillem Borràs Espert and Francisco Gómez Casanova and Luis de Pedro Sánchez and Senaida Hernández Santana and Pablo Serrano Molinero},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2607.12990},
eprint = {2607.12990},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2607.12990},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - A Noise-Aware Quantum Algorithm for Credit Valuation Adjustments on Real Quantum Hardware
AU - Guillem Borràs Espert
AU - Francisco Gómez Casanova
AU - Luis de Pedro Sánchez
AU - Senaida Hernández Santana
AU - Pablo Serrano Molinero
PY - 2026
DA - 2026/09/07
PB - arXiv
DO - 10.48550/arxiv.2607.12990
UR - https://arxiv.org/abs/2607.12990
AB - 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.
ER -