Machine learning has had success in solving real-world problems using classical computers. Since its adoption, it has undergone tremendous algorithms’ improvements. Deep learning is one of the most significant developments in this field of computer science. The data that machine-learning algorithms consume becomes more complex and keeps on growing with personal computers and mobile phones. Deep learning algorithms have been employed in data analytics to come up with trends or forecasts that can be translated into actionable results that are useful in many areas. However, these very large or complex datasets take a very long time to train. This is due to the fundamentals of classical computing operations in processing data in the basic binary of 0s and 1s. Quantum computers run on qubits, and researchers have been able to prove that they have an advantage over the current classical computers in processing of data. Therefore, this study employed experimental quantum-enhanced paradigms and aimed to take advantage of quantum-enhanced simulators currently in place. An enhanced deep learning algorithm of quantum neural network was employed in a quantum-enhanced environment. Analysis of datasets on Kenyan economy indicators from the World Bank was used to evaluate the performance and how fast actionable results and economic growth forecasts can be obtained. The data was transformed to a dimension of quantum vector with quantum mapping techniques to allow for the data dimensions to be within the boundaries for quantum computers. Quantum composer was used to create the quantum gates that processed the information and thereafter evaluation of the study was done on real quantum-enhanced simulators. When compared to the artificial neural network model, the quantum-enhanced neural network model showed a computing time reduction of around 97.7%, indicating a startling gain in effectiveness.