├── README.md ├── partially_corrupted_labels ├── 4_training_data │ ├── BEST_PARAMS_j1j2_4_0 │ ├── BEST_PARAMS_j1j2_4_1 │ ├── BEST_PARAMS_j1j2_4_2 │ ├── BEST_PARAMS_j1j2_4_3 │ ├── BEST_PARAMS_j1j2_4_4 │ ├── J1coef_j1j2_4 │ ├── J2coef_j1j2_4 │ ├── LABELS_4_0 │ ├── LABELS_4_1 │ ├── LABELS_4_2 │ ├── LABELS_4_3 │ ├── LABELS_4_4 │ └── train_groundstates.npy ├── 6_training_data │ ├── BEST_PARAMS_j1j2_6_0 │ ├── BEST_PARAMS_j1j2_6_1 │ ├── BEST_PARAMS_j1j2_6_2 │ ├── BEST_PARAMS_j1j2_6_3 │ ├── BEST_PARAMS_j1j2_6_4 │ ├── BEST_PARAMS_j1j2_6_5 │ ├── BEST_PARAMS_j1j2_6_6 │ ├── J1coef_j1j2_6 │ ├── J2coef_j1j2_6 │ ├── LABELS_6_0 │ ├── LABELS_6_1 │ ├── LABELS_6_2 │ ├── LABELS_6_3 │ ├── LABELS_6_4 │ ├── LABELS_6_5 │ ├── LABELS_6_6 │ └── train_groundstates.npy ├── 8_training_data │ ├── BEST_PARAMS_j1j2_8_0 │ ├── BEST_PARAMS_j1j2_8_1 │ ├── BEST_PARAMS_j1j2_8_2 │ ├── BEST_PARAMS_j1j2_8_3 │ ├── BEST_PARAMS_j1j2_8_4 │ ├── BEST_PARAMS_j1j2_8_5 │ ├── BEST_PARAMS_j1j2_8_6 │ ├── BEST_PARAMS_j1j2_8_7 │ ├── BEST_PARAMS_j1j2_8_8 │ ├── J1coef_j1j2_8 │ ├── J2coef_j1j2_8 │ ├── LABELS_8_0 │ ├── LABELS_8_1 │ ├── LABELS_8_2 │ ├── LABELS_8_3 │ ├── LABELS_8_4 │ ├── LABELS_8_5 │ ├── LABELS_8_6 │ ├── LABELS_8_7 │ ├── LABELS_8_8 │ └── train_groundstates.npy ├── J1coef_j1j2_1000_Test_Set ├── J2coef_j1j2_1000_Test_Set ├── LABELS_1000_Test_Set ├── accuracy_test.py ├── full_training_6_training_data │ ├── 0 │ │ ├── LABELS_6_0 │ │ ├── main_code.py │ │ ├── run_1 │ │ │ └── ACCURACY_train_j1j2_6_250STEPS │ │ ├── run_2 │ │ │ └── ACCURACY_train_j1j2_6_500STEPS │ │ ├── run_3 │ │ │ └── ACCURACY_train_j1j2_6_500STEPS │ │ ├── run_4 │ │ │ └── ACCURACY_train_j1j2_6_500STEPS │ │ ├── run_5 │ │ │ └── ACCURACY_train_j1j2_6_500STEPS │ │ └── train_groundstates.npy │ ├── 2 │ │ ├── LABELS_6_2 │ │ ├── main_code.py │ │ ├── run_1 │ │ │ └── ACCURACY_train_j1j2_6_250STEPS │ │ ├── run_2 │ │ │ └── ACCURACY_train_j1j2_6_250STEPS │ │ ├── run_3 │ │ │ └── ACCURACY_train_j1j2_6_250STEPS │ │ ├── run_4 │ │ │ └── ACCURACY_train_j1j2_6_250STEPS │ │ ├── run_5 │ │ │ └── ACCURACY_train_j1j2_6_250STEPS │ │ └── train_groundstates.npy │ ├── 4 │ │ ├── LABELS_6_4 │ │ ├── main_code.py │ │ ├── run_1 │ │ │ └── ACCURACY_train_j1j2_6_250STEPS │ │ ├── run_2 │ │ │ └── ACCURACY_train_j1j2_6_250STEPS │ │ ├── run_3 │ │ │ └── ACCURACY_train_j1j2_6_250STEPS │ │ ├── run_4 │ │ │ └── ACCURACY_train_j1j2_6_250STEPS │ │ ├── run_5 │ │ │ └── ACCURACY_train_j1j2_6_250STEPS │ │ └── train_groundstates.npy │ └── 6 │ │ ├── LABELS_6_6 │ │ ├── main_code.py │ │ ├── run_1 │ │ └── ACCURACY_train_j1j2_6_250STEPS │ │ ├── run_2 │ │ └── ACCURACY_train_j1j2_6_250STEPS │ │ ├── run_3 │ │ └── ACCURACY_train_j1j2_6_250STEPS │ │ ├── run_4 │ │ └── ACCURACY_train_j1j2_6_250STEPS │ │ ├── run_5 │ │ └── ACCURACY_train_j1j2_6_250STEPS │ │ └── train_groundstates.npy ├── main_code.py └── test_set_1000examples.npy ├── random_labels ├── 16_qubits │ ├── 10_training_data │ │ ├── BEST_PARAMS_j1j2_10 │ │ ├── J1coef_j1j2_10 │ │ ├── J2coef_j1j2_10 │ │ ├── LABELS_10 │ │ └── train_groundstates.npy │ ├── 14_training_data │ │ ├── BEST_PARAMS_j1j2_14 │ │ ├── J1coef_j1j2_14 │ │ ├── J2coef_j1j2_14 │ │ ├── LABELS_14 │ │ └── train_groundstates.npy │ ├── 20_training_data │ │ ├── BEST_PARAMS_j1j2_20 │ │ ├── J1coef_j1j2_20 │ │ ├── J2coef_j1j2_20 │ │ ├── LABELS_20 │ │ └── train_groundstates.npy │ ├── 5_training_data │ │ ├── BEST_PARAMS_j1j2_5 │ │ ├── J1coef_j1j2_5 │ │ ├── J2coef_j1j2_5 │ │ ├── LABELS_5 │ │ └── train_groundstates.npy │ ├── 8_training_data │ │ ├── BEST_PARAMS_j1j2_8 │ │ ├── J1coef_j1j2_8 │ │ ├── J2coef_j1j2_8 │ │ ├── LABELS_8 │ │ └── train_groundstates.npy │ ├── J1coef_j1j2_1000_Test_Set │ ├── J2coef_j1j2_1000_Test_Set │ ├── LABELS_1000_Test_Set │ ├── accuracy_test.py │ ├── accuracy_train.py │ ├── main_code.py │ └── test_set_1000examples.npy ├── 32_qubits │ ├── 10_training_data │ │ ├── BEST_PARAMS_j1j2_10 │ │ ├── J1coef_j1j2_10 │ │ ├── J2coef_j1j2_10 │ │ ├── LABELS_10 │ │ └── train_groundstates.npy │ ├── 14_training_data │ │ ├── BEST_PARAMS_j1j2_14 │ │ ├── J1coef_j1j2_14 │ │ ├── J2coef_j1j2_14 │ │ ├── LABELS_14 │ │ └── train_groundstates.npy │ ├── 20_training_data │ │ ├── BEST_PARAMS_j1j2_20 │ │ ├── J1coef_j1j2_20 │ │ ├── J2coef_j1j2_20 │ │ ├── LABELS_20 │ │ └── train_groundstates.npy │ ├── 5_training_data │ │ ├── BEST_PARAMS_j1j2_5 │ │ ├── J1coef_j1j2_5 │ │ ├── J2coef_j1j2_5 │ │ ├── LABELS_5 │ │ └── train_groundstates.npy │ ├── 8_training_data │ │ ├── BEST_PARAMS_j1j2_8 │ │ ├── J1coef_j1j2_8 │ │ ├── J2coef_j1j2_8 │ │ ├── LABELS_8 │ │ └── train_groundstates.npy │ ├── J1coef_j1j2_1000_Test_Set │ ├── J2coef_j1j2_1000_Test_Set │ ├── LABELS_1000_Test_Set │ ├── accuracy_test.py │ ├── accuracy_train.py │ ├── main_code.py │ └── test_set_1000examples.npy └── 8_qubits │ ├── 10_training_data │ ├── BEST_PARAMS_j1j2_10 │ ├── J1coef_j1j2_10 │ ├── J2coef_j1j2_10 │ ├── LABELS_10 │ └── train_groundstates.npy │ ├── 14_training_data │ ├── BEST_PARAMS_j1j2_14 │ ├── J1coef_j1j2_14 │ ├── J2coef_j1j2_14 │ ├── LABELS_14 │ └── train_groundstates.npy │ ├── 20_training_data │ ├── BEST_PARAMS_j1j2_20 │ ├── J1coef_j1j2_20 │ ├── J2coef_j1j2_20 │ ├── LABELS_20 │ └── train_groundstates.npy │ ├── 5_training_data │ ├── BEST_PARAMS_j1j2_5 │ ├── J1coef_j1j2_5 │ ├── J2coef_j1j2_5 │ ├── LABELS_5 │ └── train_groundstates.npy │ ├── 8_training_data │ ├── BEST_PARAMS_j1j2_8 │ ├── J1coef_j1j2_8 │ ├── J2coef_j1j2_8 │ ├── LABELS_8 │ └── train_groundstates.npy │ ├── J1coef_j1j2_1000_Test_Set │ ├── J2coef_j1j2_1000_Test_Set │ ├── LABELS_1000_Test_Set │ ├── accuracy_test.py │ ├── accuracy_train.py │ ├── main_code.py │ └── test_set_1000examples.npy ├── random_states ├── 10_qubits │ ├── 10_training_data │ │ ├── BEST_PARAMS_j1j2_10 │ │ ├── J1coef_j1j2_10 │ │ ├── J2coef_j1j2_10 │ │ ├── LABELS_10 │ │ └── train_groundstates.npy │ ├── 14_training_data │ │ ├── BEST_PARAMS_j1j2_14 │ │ ├── J1coef_j1j2_14 │ │ ├── J2coef_j1j2_14 │ │ ├── LABELS_14 │ │ └── train_groundstates.npy │ ├── 20_training_data │ │ ├── BEST_PARAMS_j1j2_20 │ │ ├── J1coef_j1j2_20 │ │ ├── J2coef_j1j2_20 │ │ ├── LABELS_20 │ │ └── train_groundstates.npy │ ├── 5_training_data │ │ ├── BEST_PARAMS_j1j2_5 │ │ ├── J1coef_j1j2_5 │ │ ├── J2coef_j1j2_5 │ │ ├── LABELS_5 │ │ └── train_groundstates.npy │ ├── 8_training_data │ │ ├── BEST_PARAMS_j1j2_8 │ │ ├── J1coef_j1j2_8 │ │ ├── J2coef_j1j2_8 │ │ ├── LABELS_8 │ │ ├── code.py │ │ └── train_groundstates.npy │ ├── J1coef_j1j2_1000_Test_Set │ ├── J2coef_j1j2_1000_Test_Set │ ├── LABELS_1000_Test_Set │ ├── accuracy_test.py │ ├── accuracy_train.py │ ├── main_code.py │ └── test_set_1000examples.npy ├── 12_qubits │ ├── 10_training_data │ │ ├── BEST_PARAMS_j1j2_10 │ │ ├── J1coef_j1j2_10 │ │ ├── J2coef_j1j2_10 │ │ ├── LABELS_10 │ │ └── train_groundstates.npy │ ├── 14_training_data │ │ ├── BEST_PARAMS_j1j2_14 │ │ ├── J1coef_j1j2_14 │ │ ├── J2coef_j1j2_14 │ │ ├── LABELS_14 │ │ └── train_groundstates.npy │ ├── 20_training_data │ │ ├── BEST_PARAMS_j1j2_20 │ │ ├── J1coef_j1j2_20 │ │ ├── J2coef_j1j2_20 │ │ ├── LABELS_20 │ │ └── train_groundstates.npy │ ├── 5_training_data │ │ ├── BEST_PARAMS_j1j2_5 │ │ ├── J1coef_j1j2_5 │ │ ├── J2coef_j1j2_5 │ │ ├── LABELS_5 │ │ └── train_groundstates.npy │ ├── 8_training_data │ │ ├── BEST_PARAMS_j1j2_8 │ │ ├── J1coef_j1j2_8 │ │ ├── J2coef_j1j2_8 │ │ ├── LABELS_8 │ │ └── train_groundstates.npy │ ├── J1coef_j1j2_200_Test_Set_1 │ ├── J1coef_j1j2_200_Test_Set_2 │ ├── J1coef_j1j2_200_Test_Set_3 │ ├── J1coef_j1j2_200_Test_Set_4 │ ├── J1coef_j1j2_200_Test_Set_5 │ ├── J2coef_j1j2_200_Test_Set_1 │ ├── J2coef_j1j2_200_Test_Set_2 │ ├── J2coef_j1j2_200_Test_Set_3 │ ├── J2coef_j1j2_200_Test_Set_4 │ ├── J2coef_j1j2_200_Test_Set_5 │ ├── LABELS_200_Test_Set_1 │ ├── LABELS_200_Test_Set_2 │ ├── LABELS_200_Test_Set_3 │ ├── LABELS_200_Test_Set_4 │ ├── LABELS_200_Test_Set_5 │ ├── accuracy_test.py │ ├── accuracy_train.py │ ├── main_code.py │ ├── test_set_200examples_1.npy │ ├── test_set_200examples_2.npy │ ├── test_set_200examples_3.npy │ ├── test_set_200examples_4.npy │ └── test_set_200examples_5.npy └── 8_qubits │ ├── 10_training_data │ ├── BEST_PARAMS_j1j2_10 │ ├── J1coef_j1j2_10 │ ├── J2coef_j1j2_10 │ ├── LABELS_10 │ └── train_groundstates.npy │ ├── 14_training_data │ ├── BEST_PARAMS_j1j2_14 │ ├── J1coef_j1j2_14 │ ├── J2coef_j1j2_14 │ ├── LABELS_14 │ └── train_groundstates.npy │ ├── 20_training_data │ ├── BEST_PARAMS_j1j2_20 │ ├── J1coef_j1j2_20 │ ├── J2coef_j1j2_20 │ ├── LABELS_20 │ └── train_groundstates.npy │ ├── 5_training_data │ ├── BEST_PARAMS_j1j2_5 │ ├── J1coef_j1j2_5 │ ├── J2coef_j1j2_5 │ ├── LABELS_5 │ └── train_groundstates.npy │ ├── 8_training_data │ ├── BEST_PARAMS_j1j2_8 │ ├── J1coef_j1j2_8 │ ├── J2coef_j1j2_8 │ ├── LABELS_8 │ └── train_groundstates.npy │ ├── J1coef_j1j2_1000_Test_Set │ ├── J2coef_j1j2_1000_Test_Set │ ├── LABELS_1000_Test_Set │ ├── accuracy_test.py │ ├── accuracy_train.py │ ├── main_code.py │ └── test_set_1000examples.npy └── real_labels ├── 16_qubits ├── 10_training_data │ ├── BEST_PARAMS_j1j2_10 │ ├── J1coef_j1j2_10 │ ├── J2coef_j1j2_10 │ ├── LABELS_10 │ └── train_groundstates.npy ├── 14_training_data │ ├── BEST_PARAMS_j1j2_14 │ ├── J1coef_j1j2_14 │ ├── J2coef_j1j2_14 │ ├── LABELS_14 │ └── train_groundstates.npy ├── 20_training_data │ ├── BEST_PARAMS_j1j2_20 │ ├── J1coef_j1j2_20 │ ├── J2coef_j1j2_20 │ ├── LABELS_20 │ └── train_groundstates.npy ├── 5_training_data │ ├── BEST_PARAMS_j1j2_5 │ ├── J1coef_j1j2_5 │ ├── J2coef_j1j2_5 │ ├── LABELS_5 │ └── train_groundstates.npy ├── 8_training_data │ ├── BEST_PARAMS_j1j2_8 │ ├── J1coef_j1j2_8 │ ├── J2coef_j1j2_8 │ ├── LABELS_8 │ └── train_groundstates.npy ├── J1coef_j1j2_1000_Test_Set ├── J2coef_j1j2_1000_Test_Set ├── LABELS_1000_Test_Set ├── accuracy_test.py ├── accuracy_train.py ├── main_code.py └── test_set_1000examples.npy ├── 32_qubits ├── 10_training_data │ ├── BEST_PARAMS_j1j2_10 │ ├── J1coef_j1j2_10 │ ├── J2coef_j1j2_10 │ ├── LABELS_10 │ └── train_groundstates.npy ├── 14_training_data │ ├── BEST_PARAMS_j1j2_14 │ ├── J1coef_j1j2_14 │ ├── J2coef_j1j2_14 │ ├── LABELS_14 │ └── train_groundstates.npy ├── 20_training_data │ ├── BEST_PARAMS_j1j2_20 │ ├── J1coef_j1j2_20 │ ├── J2coef_j1j2_20 │ ├── LABELS_20 │ └── train_groundstates.npy ├── 5_training_data │ ├── BEST_PARAMS_j1j2_5 │ ├── J1coef_j1j2_5 │ ├── J2coef_j1j2_5 │ ├── LABELS_5 │ └── train_groundstates.npy ├── 8_training_data │ ├── BEST_PARAMS_j1j2_8 │ ├── J1coef_j1j2_8 │ ├── J2coef_j1j2_8 │ ├── LABELS_8 │ └── train_groundstates.npy ├── J1coef_j1j2_1000_Test_Set ├── J2coef_j1j2_1000_Test_Set ├── LABELS_1000_Test_Set ├── accuracy_test.py ├── accuracy_train.py ├── main_code.py └── test_set_1000examples.npy └── 8_qubits ├── 10_training_data ├── BEST_PARAMS_j1j2_10 ├── J1coef_j1j2_10 ├── J2coef_j1j2_10 ├── LABELS_10 └── train_groundstates.npy ├── 14_training_data ├── BEST_PARAMS_j1j2_14 ├── J1coef_j1j2_14 ├── J2coef_j1j2_14 ├── LABELS_14 └── train_groundstates.npy ├── 20_training_data ├── BEST_PARAMS_j1j2_20 ├── J1coef_j1j2_20 ├── J2coef_j1j2_20 ├── LABELS_20 └── train_groundstates.npy ├── 5_training_data ├── BEST_PARAMS_j1j2_5 ├── J1coef_j1j2_5 ├── J2coef_j1j2_5 ├── LABELS_5 └── train_groundstates.npy ├── 8_training_data ├── BEST_PARAMS_j1j2_8 ├── J1coef_j1j2_8 ├── J2coef_j1j2_8 ├── LABELS_8 └── train_groundstates.npy ├── J1coef_j1j2_1000_Test_Set ├── J2coef_j1j2_1000_Test_Set ├── LABELS_1000_Test_Set ├── accuracy_test.py ├── accuracy_train.py ├── main_code.py └── test_set_1000examples.npy /README.md: -------------------------------------------------------------------------------- 1 | # Understanding quantum machine learning also requires rethinking generalization 2 | 3 | This repository contains the data and code in the paper "Understanding quantum machine learning also requires rethinking generalization," found in [Nature Communications](https://www.nature.com/articles/s41467-024-45882-z) or [arXiv](https://arxiv.org/abs/2306.13461). The code relies on the following packages: TensorCircuit [(GitHub)](https://github.com/tencent-quantum-lab/tensorcircuit) and Qibo [(GitHub)](https://github.com/qiboteam/qibo). Please ensure that these packages are installed before running the code. 4 | 5 | 6 | ## Repository structure 7 | 8 | The repository is organized into folders corresponding to different experiments conducted. Three primary code files can be executed: 9 | 10 | 1. `main_code.py`: This file trains and executes the quantum convolutional neural network from scratch for the different experiments. It accepts the following arguments: 11 | 12 | - `--training_data` (int): Training data size. It supports `5`, `8`, `10`, `14`, and `20`. Default = `5`. 13 | - `--accuracy_training` (int): minimum training accuracy that shall be achieved. The code performs new random initialization and training if the accuracy falls below this threshold. Default = `100` 14 | 15 | Note that executing `main_code.py` can be computationally demanding. To reproduce the results presented in the paper, consider running the following files instead. 16 | 17 | 2. `accuracy_train.py`: Run this file to obtain the training accuracy using the best parameters determined by the authors. It accepts the same `--training_data` argument as `main_code.py`. 18 | 19 | 3. `accuracy_test.py`: Run this file to obtain the test accuracy using the best parameters determined by the authors. Again, it accepts the same `--training_data` argument as `main_code.py`. 20 | 21 | 22 | ## Running the code 23 | 24 | To run the code, follow these steps: 25 | 26 | 1. Install the required packages: TensorCircuit and Qibo. 27 | 28 | 2. Choose the appropriate code file based on your requirements: 29 | - To train and execute the quantum convolutional neural network from scratch, run `main_code.py`. 30 | - For reproducing the paper's results, execute `accuracy_train.py` on the training set and `accuracy_test.py` on the test set. 31 | 32 | 3. Set the desired values for the arguments `--training_data` and `--accuracy_training` (if applicable) to customize the execution. 33 | 34 | 4. Execute the chosen code file, ensuring the required packages are accessible. 35 | 36 | For further assistance or inquiries, please refer to the paper or contact the authors directly. 37 | -------------------------------------------------------------------------------- /partially_corrupted_labels/4_training_data/BEST_PARAMS_j1j2_4_0: -------------------------------------------------------------------------------- 1 | -4.845781053053165088e+00 -6.897922025895069531e+00 3.076176960394279458e+00 -6.347142374135145815e+00 6.469020514589103321e+00 4.216677055424880471e+00 1.813907395778910470e+01 -5.176947367350510198e+00 7.583772213224393433e+00 3.050578716193608653e+00 9.291802584025166567e+00 -9.400958222135447073e-01 6.008725468151640214e+00 3.059080383983480367e+00 7.979317887110315866e+00 -5.534210685965567755e+00 3.858052043884915516e+00 4.704596312425531224e+00 -3.230373230613755364e+00 -3.611131241341079434e+00 2.944707407792289899e+00 7.687266832816656681e+00 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-------------------------------------------------------------------------------- 1 | #!/usr/bin/env python3 2 | # -*- coding: utf-8 -*- 3 | 4 | import argparse 5 | import numpy as np 6 | from qibo.symbols import Z, I 7 | from qibo import hamiltonians 8 | from qibo import models, gates 9 | import qibo 10 | qibo.set_backend("numpy") 11 | 12 | def main(training_data): 13 | def MPO_3(): 14 | symbolic_expr = Z(3)*I(7) 15 | hamiltonian = hamiltonians.SymbolicHamiltonian(form=symbolic_expr) 16 | return hamiltonian 17 | 18 | def MPO_7(): 19 | symbolic_expr = Z(7) 20 | hamiltonian = hamiltonians.SymbolicHamiltonian(form=symbolic_expr) 21 | return hamiltonian 22 | 23 | 24 | def MPO_37(): 25 | symbolic_expr = Z(3)*Z(7) 26 | hamiltonian = hamiltonians.SymbolicHamiltonian(form=symbolic_expr) 27 | return hamiltonian 28 | 29 | def convolutional_layer(c, q1, q2, param): 30 | c.add(gates.U3(q1, theta=param[0], phi=param[1], lam=param[2])) 31 | c.add(gates.U3(q1, theta=param[3], phi=param[4], lam=param[5])) 32 | c.add(gates.CU1(q1, q2, theta=param[6])) 33 | c.add(gates.U3(q1, theta=param[7], phi=param[8], lam=param[9])) 34 | c.add(gates.U3(q1, theta=param[10], phi=param[11], lam=param[12])) 35 | return c 36 | 37 | def pooling_layer(c, q1, q2, param): 38 | c.add(gates.CU3(q1, q2, theta=param[0], phi=param[1], lam=param[2])) 39 | return c 40 | 41 | def accuracy(params): 42 | accuracy_data = 0 43 | circuit = models.Circuit(nqubits) 44 | 45 | convolutional_layer(circuit, 0, 1, params[:13]) 46 | convolutional_layer(circuit, 2, 3, params[:13]) 47 | convolutional_layer(circuit, 4, 5, params[:13]) 48 | convolutional_layer(circuit, 6, 7, params[:13]) 49 | 50 | convolutional_layer(circuit, 1, 2, params[:13]) 51 | convolutional_layer(circuit, 3, 4, params[:13]) 52 | convolutional_layer(circuit, 5, 6, params[:13]) 53 | convolutional_layer(circuit, 7, 0, params[:13]) 54 | 55 | pooling_layer(circuit, 0, 1, params[13:16]) 56 | pooling_layer(circuit, 2, 3, params[13:16]) 57 | pooling_layer(circuit, 4, 5, params[13:16]) 58 | pooling_layer(circuit, 6, 7, params[13:16]) 59 | 60 | convolutional_layer(circuit, 1, 3, params[16:29]) 61 | convolutional_layer(circuit, 5, 7, params[16:29]) 62 | 63 | convolutional_layer(circuit, 3, 5, params[16:29]) 64 | convolutional_layer(circuit, 7, 1, params[16:29]) 65 | 66 | pooling_layer(circuit, 1, 3, params[29:32]) 67 | pooling_layer(circuit, 5, 7, params[29:32]) 68 | 69 | convolutional_layer(circuit, 3, 7, params[32:45]) 70 | 71 | for j in range(1000): 72 | print(j) 73 | final_state = circuit(gs_list[j]).state() 74 | 75 | z3 = np.real(ham3.expectation(final_state)) 76 | z7 = np.real(ham7.expectation(final_state)) 77 | zz37 = np.real(ham37.expectation(final_state)) 78 | 79 | proj_00 = (1+zz37+z3+z7)/4 80 | proj_01 = (1-zz37-z3+z7)/4 81 | proj_10 = (1-zz37+z3-z7)/4 82 | proj_11 = (1+zz37-z3-z7)/4 83 | 84 | if label_list[j] == 0: 85 | if proj_00 < proj_01 and proj_00 < proj_10 and proj_00 < proj_11: 86 | accuracy_data += 1 87 | elif label_list[j] == 1: 88 | if proj_01 < proj_00 and proj_01 < proj_10 and proj_01 < proj_11: 89 | accuracy_data += 1 90 | elif label_list[j] == 2: 91 | if proj_10 < proj_00 and proj_10 < proj_01 and proj_10 < proj_11: 92 | accuracy_data += 1 93 | else: 94 | if proj_11 < proj_00 and proj_11 < proj_01 and proj_11 < proj_10: 95 | accuracy_data += 1 96 | 97 | 98 | return accuracy_data*100/1000 99 | 100 | nqubits = 8 101 | best_params = np.loadtxt(f"{training_data}_training_data/BEST_PARAMS_j1j2_{training_data}") 102 | 103 | label_list = np.loadtxt('LABELS_1000_Test_Set') 104 | 105 | gs_list = np.load('test_set_1000examples.npy', allow_pickle=True) 106 | 107 | ham7 = MPO_7() 108 | ham3 = MPO_3() 109 | ham37 = MPO_37() 110 | 111 | train_accuracy = accuracy(best_params) 112 | print(train_accuracy) 113 | 114 | 115 | if __name__ == "__main__": 116 | parser = argparse.ArgumentParser() 117 | parser.add_argument("--training_data", default=5, type=int) 118 | args = parser.parse_args() 119 | main(**vars(args)) 120 | -------------------------------------------------------------------------------- /random_states/8_qubits/test_set_1000examples.npy: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/bpcarlos/understanding_QML_rethinking_gen/7e40f7a6f0cfc00d0ff6f53c1154622ca80997d8/random_states/8_qubits/test_set_1000examples.npy -------------------------------------------------------------------------------- /real_labels/16_qubits/10_training_data/BEST_PARAMS_j1j2_10: -------------------------------------------------------------------------------- 1 | -7.477087080919099238e-01 1.043877444871051097e+01 2.076707223838754057e+00 -1.934366522688267143e+00 8.681493051888734769e+00 3.639814241632076897e+00 -3.990506640980122643e+00 -3.232498642573145009e-01 7.217863408549751725e+00 -7.490089979258754305e+00 8.476152512238680714e+00 7.134286107843329283e+00 9.304366209271215382e+00 8.685553839043267388e+00 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