Binary Classifier Inspired by Quantum Theory

\ac{ML} helps us to recognize patterns from raw data. \ac{ML} is used in numerous domains i.e. biomedical, agricultural, food technology, etc. Despite recent technological advancements, there is still room for substantial improvement in prediction. Current \ac{ML} models are based on classical theories of probability and statistics, which can now be replaced by \ac{QT} with the aim of improving the effectiveness of \ac{ML}. In this paper, we propose the \ac{BCIQT} model, which outperforms the state of the art classification in terms of recall for every category.