Binary classification of single qubits using quantum machine learning method

Machine learning can make predictions on unseen data by extracting information and constructing algorithms and statistical models from the training data, which is highly desirable for speech recognition and computer vision. Quantum computing harnesses the phenomena of quantum mechanics realizing parallel computing, which could solve certain computational problems, such as RSA encryption significantly faster than classical computers. Quantum machine learning is reasonable to enable machine learning faster than that of classical computers by utilizing quantum operations. Binary classification is a typical quantum machine learning task applied to single qubits. Here we present a binary classifier for regions. Firstly, we sample two sets of quantum data points located in the X-Z plane of the Bloch sphere randomly. Then we utilize a single parameterized rotation gate and measure the qubits along the Z axis. We train the hybrid model combined by quantum mdoel and classical Neutral network and regard the corss entropy the predictions of the classical Netural network and labels as the loss function. Based on the above, we present the results of the trained hybrid model to classify new quantum qubits.