Toward Quantum Monte Carlo for Sovereign Debt Risk: A proof of concept
Abstract
We present a proof-of-concept pipeline that applies quantum amplitude estimation to the probability that a sovereign debt-to-GDP ratio exceeds a critical threshold at a given horizon. The pipeline combines a reduced two-factor Gaussian debt model linearized around its mean, quantum state preparation on a 32 x 32 grid, a weighted-adder circuit, an integer comparator, and Maximum Likelihood Amplitude Estimation (MLAE). On a noiseless statevector simulator with 500 shots per Grover power, the circuit estimate (a = 0.1152) agrees with the circuit's exact target amplitude (0.1153), and the observed estimation error is consistent with the amplitude-estimation scaling in the number of oracle calls. The model used here is deliberately simple: it admits a closed-form solution, so the contribution is a demonstration of the encoding and estimation workflow, not a practical advantage over classical methods. We discuss the conditions under which such a pipeline could become relevant and the obstacles that remain.
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BibTeX
@misc{dessureault2026toward,
title = {{Toward Quantum Monte Carlo for Sovereign Debt Risk: A proof of concept}},
author = {Jean-Sébastien Dessureault and Alitiana Mijoro Barisoa},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.41831},
eprint = {2610.41831},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.41831},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Toward Quantum Monte Carlo for Sovereign Debt Risk: A proof of concept
AU - Jean-Sébastien Dessureault
AU - Alitiana Mijoro Barisoa
PY - 2026
DA - 2026/10/30
PB - arXiv
DO - 10.48550/arxiv.2610.41831
UR - https://arxiv.org/abs/2610.41831
AB - We present a proof-of-concept pipeline that applies quantum amplitude estimation to the probability that a sovereign debt-to-GDP ratio exceeds a critical threshold at a given horizon. The pipeline combines a reduced two-factor Gaussian debt model linearized around its mean, quantum state preparation on a 32 x 32 grid, a weighted-adder circuit, an integer comparator, and Maximum Likelihood Amplitude Estimation (MLAE). On a noiseless statevector simulator with 500 shots per Grover power, the circuit estimate (a = 0.1152) agrees with the circuit's exact target amplitude (0.1153), and the observed estimation error is consistent with the amplitude-estimation scaling in the number of oracle calls. The model used here is deliberately simple: it admits a closed-form solution, so the contribution is a demonstration of the encoding and estimation workflow, not a practical advantage over classical methods. We discuss the conditions under which such a pipeline could become relevant and the obstacles that remain.
ER -