Quantum Counterparty Credit Risk: A Study of Path-Dependent Derivatives

• URL: http://arxiv.org/abs/2606.28701v1
• Date: Sat, 27 Jun 2026 03:03:44 GMT
• Title: Quantum Counterparty Credit Risk: A Study of Path-Dependent Derivatives
• Abstract summary: We present a hybrid quantum-classical framework to estimate future exposure for path-dependent derivatives.<n>We employ discretisation of the FX process and a linearised approximation to enable implementation on current platforms.<n>Our framework offers a tractable testbed for quantum acceleration.
• Score: 1.813553694972824
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Estimating potential future exposure (PFE) for path-dependent derivatives, such as FX Target Redemption Forwards (TARFs), represents a formidable computational challenge due to the demand of nested Monte Carlo simulations. We present a hybrid quantum-classical framework that leverages Iterative Quantum Amplitude Estimation (IQAE) to address this via a reduced-order counterparty credit risk model. Our methodology maps the non-linear TARF payoff -- including cumulative gains and knock-out features -- into a quantum circuit via a two-step formulation, whereby a first-step percentile is computed classically and then used to condition quantum evaluation of subsequent exposure. We employ discretisation of the FX process and a linearised additive approximation of dynamics to enable implementation on current quantum platforms. Developed via the Classiq platform and validated on NVIDIA CUDA-Q and Amazon Braket SV1, our approach achieves relative errors of 1%-8% against classical benchmarks at the 97.5% and 99% confidence levels. While discretisation constraints and approximate monotonicity assumption may introduce bias and limit recovery of the full exposure distribution, our framework offers a tractable testbed for quantum acceleration. Scaling analysis suggests that $\sim$300 logical qubits could enable full 52-week exposure estimation, reducing sample complexity for tail-risk estimation via amplitude estimation at the cost of increased circuit depth.

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