International Conference on Next-Gen Quantum and Advanced Computing: Algorithms, Security, and Beyond (NQComp)

On Representation Error in Quantum Option Pricing: A Diagnostic Study of the Standard Qiskit Implementation

K Soveet Kumar Prusty, Kartikeyan Premkumar, Shweta S. Aladakatti, K Madhura

Published Mar 5, 2026• 0 citations

Abstract

Quantum Amplitude Estimation (QAE) is an attractive financial derivative pricing algorithm with the potential for a quadratic speedup over classical Monte Carlo approaches. Yet, quantum advantage will only be found if not just fault-tolerant hardware but also an accurate representation of the financial problem on a quantum circuit is achieved. This paper offers a reproducible, diagnostic examination of the default Qiskit Finance application to European option pricing. We present a critical diagnostic marker: the classical discretized price, which is the classically computed expected payoff on the quantum model's actual grid. Our convergence analysis of low-qubit models (4, 5, and 6 uncertainty qubits) shows that for this particular configuration, representation error, which arises due to imprecise trigonometric encoding of the option payoff function, is the prevalent source of error, much larger compared to the discretization error. In a 6-qubit model, the representation error was 0.17699 USD, which is an order of magnitude larger than the discretization error of -0.01200 USD. We illustrate how a different Piecewise Linear (PWL) encoding can successfully remove this representation error, but at the expense of more circuit resources. Our results, for the vanilla, low-qubit Qiskit Finance circuits we used, indicate that the creation of high-fidelity, resource-conscious problem encodings is a pressing and essential problem on the road to applicable quantum advantage in finance.

DOI: 10.1109/nqcomp68334.2026.11497710

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