Technological advancements in the field of quantum computing and the emergence of hybrid quantum–classical algorithms have created new opportunities for addressing complex financial and investment-related problems. One of the most significant challenges in the evaluation of smart infrastructure projects is the presence of multidimensional uncertainties associated with investment costs, market demand, interest rates, technological changes, and governmental policies. Traditional valuation methods, such as Discounted Cash Flow (DCF) analysis, are unable to fully capture the value of strategic opportunities embedded in these projects due to their assumption of managerial inflexibility. In contrast, Real Options Theory provides a more appropriate framework for analyzing investment decisions under uncertainty by explicitly incorporating managerial flexibility into the valuation process.
The objective of this study is to develop a novel framework for pricing real options in smart infrastructure projects through the integration of hybrid quantum–classical algorithms and advanced Monte Carlo simulation techniques. Within the proposed framework, the stochastic variables affecting project value are first modeled to represent the underlying sources of uncertainty. Subsequently, potential project value trajectories are generated using multilevel and quasi-random Monte Carlo methods. The theoretical findings indicate that the integration of quantum computing techniques with advanced simulation approaches can significantly improve valuation accuracy while substantially reducing computational costs compared to conventional classical methods. Furthermore, the proposed framework is applicable to a wide range of domains, including smart city projects, smart energy networks, intelligent transportation systems, and digital infrastructure developments. By establishing a connection between engineering economics, computational finance, and quantum computing, this research provides a new pathway for investment decision-making in complex and dynamic environments.