Data for "A Permutation-equivariant Deep Learning Model for Quantum State Characterization" The repository contains the data supporting the findings contained in D. Maragnano, M. Liscidini, C. Cusano, "A Permutation-equivariant Deep Learning Model for Quantum State Characterization" (submitted to APL Machine Learning). Data is stored in compressed NumPy archives. The data is organized as follows. 2qubit_data 2qubit_features: it contains the input features to train, validate, and test the 2-qubit models. The names of the files are structured according to the following convention. features_{a}_2q_noiseless.npz: noiseless features for train, validation, and test. features_{a}_2q_{b}{c}.npz: noisy features for train, validation, and test, for different noisy channels and different noise strengths. a ={train, test, valid} for train, test, and validation, respectively. b = {depol, stateimperf} for noiseless features, features affected by depolarizing or state-error imperfection, respectively. c = {001, 005, 01} for noise strength equal to 0.01, 0.05, and 0.1, respectively. Files where a = train contain an array with shape (21600, 16), where each row records the measurement outcomes of a single density matrix. Files where a = validation, test contain an array with shape (1200, 16), where each row records the measurement outcomes of a single density matrix. 2qubit_predictions: it contains two subdirectories. predictions_purity_2q: it contains the predictions of the purity of the Linear and PELinear models. The names of the files are structured according to the following convention. {m}_predictions_noiseless_purs_2qubit.npz: purity predictions when the model is trained on noiseless features. {m}_predictions_{n}{c}_purs_2qubit.npz: purity predictions when the model is trained on noisy features. m = {linear, pelinear}: Linear and PELinear modelm respectively. n = {noisyd, noisysi}: the model is trained on features affected by depolarizing or state-error imperfection noise, respectively. c = {001, 005, 01}: noise strength equal to 0.01, 0.05, and 0.1, respectively. Each file contains an array with shape (1200, 1). Each row represents the purity prediction of a single density matrix. predictions_tqst_2q: it contains the preditcions of the density matrices of the Linear and PELinear models. The names of the files are structured as follows: {m}_predictions_noiseless_qst_2qubit.npz: it contains the predictions of the density matrices when the model is trained on noiseless features. {m}_predictions_{n}{c}_qst_2qubit.npz: it contains the predictions of the density matrices when the model is trained on noisy features. m = {linear, pelinear}: Linear and PELinear model, respectively. n = {noisyd, noisysi}: the model is trained on features affected by depolarizing or state-error imperfection noise, respectively. c = {001, 005, 01}: noise strength equal to 0.01, 0.05, and 0.1, respectively. Each file contains an array with shape (1200, 16). Each row represents the prediction of a single density matrix. Three files named purs_{a}_2q.npz, with a ={train, test, valid}, containing the target purities for train, test, and validation, with shapes (21600,), (1200,), (1200,), respectively. Each element is the target purity of a single density matrix. Three files named vecs_{a}_2q.npz, with a ={train, test, valid}, containing the target density matrices for train, test, and validation, with shapes (21600, 16), (1200, 16), (1200, 16), respectively. Each row of the array contains the real parameter describing a single density matrix. We provide a custom-made function to convert these arrays into density matrices in the file from_array_to_dms.py. A file from_array_to_dms.py with a custom-made Python function to convert one-dimensional arrays into a density matrix. 4qubit_data 4qubit_features: it contains the input features to train, validate, and test the 2-qubit models. The names of the files are structured according to the following convention. features_{a}_4q_noiseless.npz: noiseless features for train, validation, and test. features_{a}_4q_{b}{c}.npz: noisy features for train, validation, and test, for different noisy channels and different noise strengths. a ={train, test, valid} for train, test, and validation, respectively. b = {depol, stateimperf} for noiseless features, features affected by depolarizing or state-error imperfection, respectively. c = {001, 005, 01, 02, 04, 06, 08, 1} for noise strength equal to 0.01, 0.05, 0.1, 0.2, 0.4, 0.6, 0.8, 1, respectively. Files where a = train contain an array with shape (108000, 256), where each row records the measurement outcomes of a single density matrix. Files where a = validation, test contain an array with shape (6000, 256), where each row records the measurement outcomes of a single density matrix. 4qubit_predictions: it contains two subdirectories. predictions_purity_4q: it contains the predictions of the purity of the combined model. The names of the files are structured according to the following convention. predictions_purs_4qubit_noiseless.npz: purity predictions when the model is trained on noiseless features. predictions_purs_4qubit_{n}{c}.npz: purity predictions when the model is trained on noisy features. n = {noisyd, noisysi}: the model is trained on features affected by depolarizing or state-error imperfection noise, respectively. c = {001, 005, 01, 02, 04, 06, 08, 1} for noise strength equal to 0.01, 0.05, 0.1, 0.2, 0.4, 0.6, 0.8, 1, respectively. Each file contains an array with shape (6000, 1). Each row represents the purity prediction of a single density matrix. predictions_tqst_4q: it contains the preditcions of the density matrices of the combined model. The names of the files are structured as follows. predictions_noiseless_qst_4qubit.npz: it contains the predictions of the density matrices when the model is trained on noiseless features. predictions_{n}{c}_qst_4qubit.npz: it contains the predictions of the density matrices when the model is trained on noisy features. n = {noisyd, noisysi}: the model is trained on features affected by depolarizing or state-error imperfection noise, respectively. c = {001, 005, 01, 02, 04, 06, 08, 1} for noise strength equal to 0.01, 0.05, 0.1, 0.2, 0.4, 0.6, 0.8, 1, respectively. Each file contains an array with shape (6000, 256). Each row represents the prediction of a single density matrix. Three files named purs_{a}_4q.npz, with a ={train, test, valid}, containing the target purities for train, test, and validation, with shapes (108000,), (6000,), (6000,), respectively. Each element is the target purity of a single density matrix. Three files named vecs_{a}_4q.npz, with a ={train, test, valid}, containing the target density matrices for train, test, and validation, with shapes (108000, 256), (6000, 256), (6000, 256), respectively. Each row of the array contains the real parameter describing a single density matrix. We provide a custom-made function to convert these arrays into density matrices in the file *from_array_to_dms.py*. A file from_array_to_dms.py with a custom-made Python function to convert one-dimensional arrays into a density matrix.