QUANTUM-ENHANCED NEURAL NETWORK FOR FORECASTING KENYAN ECONOMIC GROWTH

Abstract

Machine learning has had success in solving real world problems using classical computers. Since its adoption, it has undergone tremendous algorithms improvements. One of the most important advancements in this area of computer science is deep learning. Deep learning has already outperformed humans in a number of fields, e.g stock market forecasting. On the other hand, the data that machine learning algorithms consume becomes more complex and keeps on growing through the use of personal computers and mobile phones. Deep learning algorithms have been employed in these data analytics to come up with trends or classifications 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 to develop a quantum-enhanced neural network model to forecast Kenyan economic growth. It took advantage of quantum-enhanced simulators, currently in place to experiment on the efficiency of data analysis. Analysis on Kenyan economy indicators datasets 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 to allow for the data dimensions to be within the boundaries for quantum computers. The quantum-enhanced neural network model demonstrated a computing time reduction of approximately 97.7% when compared to the artificial neural network model, demonstrating a remarkable increase in efficiency. The model mean absolute error indicated a relatively small average deviation of 0.01047 from the actual values. In addition, the mean squared error indicated a low average squared deviation of 0.00025 from the true values. The discrepancy from the expected and actual values was 0.99775, which indicated a high degree of predictability and a strong fit of the model to the data. The quantum-enhanced neural network model demonstrated an overall good performance in all areas of the forecast study.