Currently quantum computing technology is still predominantly experimental with several obstacles to its practical application. One of the main challenges for the adoption of quantum computing is the fragile state of qubits. Existing technologies can only maintain information in a quantum state for short periods of time, which limits the duration of operations in practice.

For this reason, errors and decoherence are inherent phenomena in current quantum computers. Despite the development of initial proposals for error correction techniques, the persistence of errors continues to make the execution of complex algorithms on a sufficient number of qubits challenging, as errors tend to dominate the results.

Therefore, existing quantum computers are not capable of performing complex calculations on a large scale without producing errors and noise that can affect the quality of the results obtained. This phenomenon reflects the evolution of quantum computers and the era in which we find ourselves, defining these devices as “noisy intermediate-scale quantum.” This is one of the reasons why the experiments carried out for this work were performed on a simulator rather than on actual quantum hardware.

Since seminal work of Black and Scholes in 1973 on estimating the price of a plain vanilla european financial option using the partial differential equation of the same name, several methodologies have been developed for valuing these instruments, such as the binomial and trinomial methodologies and the Monte Carlo method, among others.

In this work, the necessary quantum computing algorithms and simulation tools will be used to determine the call option premium, since access to a quantum computer is currently prohibitively expensive, making it unattainable for almost everyone who lacks the necessary resources.

The objective of this paper is to perform the valuation of a financial vanilla call option using the quantum computing tool with option parameters for 2025 and compare it with traditional methodologies. The structure of this article is as follows: section 2 reviews the most recent literature on quantum computing and financial options, while section 3 presents the background of the quantum computing framework used for the valuation model make a review related to Black Scholes financial options and their relationship with Monte Carlo simulations. The Section 4 presents the proposed quantum computing model for evaluating financial options. Finally, section 5 presents the results obtained and finished with the section 6 conclusions.

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