Codes&Models

The dataset consists of two sections. The first section pertains to quantum states composed of two qubits, with files in this section labeled with the prefix “QBQB.” The second section covers qubit-qutrit states, with files beginning with “QBQT.” Each section includes three Python files: gen_data_neg, NN, and test_NN. These files are responsible for generating measurement data for the neural network, training the neural network, and testing the neural network, respectively.\nAdditional files in each section contain the trained models for different sets of measurement outcomes, named in the format “model_neg_p#_i500K_NN_32_16.” Here, “p#” denotes the number of measurements conducted, “i500K” signifies that the neural network was trained on 500,000 random quantum states, and “NN_32_16” indicates the neural network’s architecture, with two hidden layers containing 32 and 16 nodes. Each Python file includes code comments for guidance.