This paper explores a quantum machine learning model for the binary classification applied to larger datasets. This binary classifier acts on density matrices, which are the quantum encoding of classic samples of a dataset. The model's effectiveness is due to the increase of quantum copies of a pattern and the subsequent tensor product. The primary objective of the study is to investigate the Quantum Binary Classifier that utilizes two dimensions qubits. The algorithm is developed based on a hybrid scheme incorporating Pennylane Variational Classifier. For encoding, Pennylane amplitude encoding is used. The quantum binary classifier is tested on a synthetic dataset, demonstrating that the quantum method can effectively accomplish the task with only 2 qubits. The accuracy is as good as the classical classifiers.