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.