├── .gitignore ├── README.md ├── colors ├── colors.ipynb ├── colors.pdf ├── colors_1D.pkl ├── colors_2D.pkl └── colors_dict.pkl ├── figures ├── fig-1.3 │ ├── 1.3-title.ipynb │ ├── ML_papers_data.csv │ └── fig1-3.pdf ├── fig-2.1 │ ├── 2.1-Examples_of_loss_functions.ipynb │ └── fig-2.1.pdf ├── fig-4.1 │ ├── 4.1-title.ipynb │ └── 4.1_toy_example_corr.pdf ├── fig-4.2 │ ├── 4.2-title.ipynb │ └── 4.2_kernel_trick_comparison_corr.pdf ├── fig-4.6 │ ├── 4.6-title.ipynb │ ├── 4.6-title_v2.ipynb │ └── figure4.6.pdf ├── fig-4.7 │ └── 4.7-kernel-search-Alexandre-Dauphin.pdf ├── fig-6.4 │ ├── 6.4-title.ipynb │ ├── fig-6.4b_walker_convergence.pdf │ └── fig6-4.pdf ├── fig-7.4 │ ├── 7.4-title.ipynb │ └── figure7.4.pdf ├── fig-8.11 │ └── quantum_circuit_with_measurement.pdf ├── fig-8.16 │ ├── 8.16b-optimization.ipynb │ └── panel-a-Paolo-Stornati.pdf ├── fig-8.4 │ ├── 8.4_covers_theorem.ipynb │ └── perceptron_capacity.pdf └── graphical-designer-FESIDO │ ├── 1.1_TP_vs_ML.pdf │ ├── 1.2_AIvsMLvsDL.pdf │ ├── 1.4_interplay_of_AI.pdf │ ├── 1.5_Content_of_these_Lecture_Notes.pdf │ ├── 1.6_Dependency_tree.pdf │ ├── 2.10_Backpropagation.pdf │ ├── 2.2_choosing_a_learning_rate.pdf │ ├── 2.3_under_and_over_fitting.pdf │ ├── 2.4_bias-variance_trade-off.pdf │ ├── 2.5_geometric_SVM.pdf │ ├── 2.6_NN_and_neuron.pdf │ ├── 2.7_CNN.pdf │ ├── 2.8_autoencoder.pdf │ ├── 2.9_RNN.pdf │ ├── 3.3a_PCA.pdf │ ├── 3.5a_Unsupervised_phase_class_AE.pdf │ ├── 3.9b_Siamese_bottleneck_interpretation.pdf │ ├── 4.3_Linear_SVM_applied.pdf │ ├── 4.4_bayesian_neural_network.pdf │ ├── 4.5_BO+GPR.pdf │ ├── 4.8 Three classes of problems for GPs.pdf │ ├── 4.9 feedback_loops.pdf │ ├── 5.1 RBM.pdf │ ├── 5.2 ARNN state.pdf │ ├── 5.3 RNN for q. state.pdf │ ├── 5.4 Expressive power of NQS.pdf │ ├── 5.5 Schemes of ansatzes_2x2.pdf │ ├── 5.5 Schemes of ansatzes_4x1.pdf │ ├── 6.1 Overview of RL setting.pdf │ ├── 6.10 RL relaxation.pdf │ ├── 6.2_Exploration_exploitation.pdf │ ├── 6.3 Projective simulation.pdf │ ├── 6.6 Driven single mode microcavity for RL.pdf │ ├── 6.7 RL circuit optimization.pdf │ ├── 6.8 RL-based error correction.pdf │ ├── 7.14_Illustration_of_the_one-spin_Hamiltonian_learning.pdf │ ├── 7.17_AI-design_of_experiments.pdf │ ├── 7.1_ML_influences_physics.pdf │ ├── 7.2 Standard vs. differentiable programming.pdf │ ├── 7.5_Sketch_of_a_normalizing_flow.pdf │ ├── 7.6_volume_transformation.pdf │ ├── 8.12 Quantum machine learning.pdf │ ├── 8.13 Realization of the famous Shor algorithm in a real quantum computer.pdf │ ├── 8.14a Quantum SVM enhanced by a quantum device scheme (tylko panel a).pdf │ ├── 8.15 Variational optimization of quantum circuits.pdf │ ├── 8.1_Physics_influences_ML.pdf │ ├── 8.2 Statistical physics toolbox for understanding ML theory.pdf │ ├── 8.3 U and double descent.pdf │ └── 8.6 Scheme of a two-layer committee machine.pdf ├── fonts ├── Hero New Bold Italic.otf ├── Hero New Bold.otf ├── Hero New ExtraBold Italic.otf ├── Hero New ExtraBold.otf ├── Hero New Hairline Italic.otf ├── Hero New Hairline.otf ├── Hero New Light Italic.otf ├── Hero New Light.otf ├── Hero New Medium Italic.otf ├── Hero New Medium.otf ├── Hero New Regular Italic.otf ├── Hero New Regular.otf ├── Hero New SemiBold Italic.otf ├── Hero New SemiBold.otf ├── Hero New Super Italic.otf ├── Hero New Super.otf ├── Hero New Thin Italic.otf ├── Hero New Thin.otf ├── Hero New UltraLight Italic.otf ├── Hero New UltraLight.otf ├── font_examples.png └── fonts.ipynb └── tex_files ├── arXiv_v1.zip ├── arXiv_v2.zip ├── arXiv_v3.zip └── arXiv_v4.zip /.gitignore: -------------------------------------------------------------------------------- 1 | 2 | .ipynb_checkpoints/ 3 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # This is a repository for figures and tex files of "Modern applications of machine learning in quantum sciences" 2 | 3 | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](http://colab.research.google.com/github/Shmoo137/Lecture-Notes/) 4 | 5 | ### List of our figures: 6 | - 1.3 % of ML-based articles in the selected fields in years 2000-2021 - A. Dawid 7 | - 2.1 Plots of the (a) binary cross-entropy and (b) mean-squared error - A. Dawid 8 | - 3.1 Ising model (to reproduce, go to the Notebook A1 from the [school GitHub](https://github.com/Shmoo137/SummerSchool2021_MLinQuantum)) - R. Koch 9 | - 3.2 IGT (to reproduce, go to the Notebook A1 from the [school GitHub](https://github.com/Shmoo137/SummerSchool2021_MLinQuantum)) - R. Koch 10 | - 3.3bc PCA (to reproduce, go to the Notebook A1 from the [school GitHub](https://github.com/Shmoo137/SummerSchool2021_MLinQuantum)) - R. Koch 11 | - 3.6 Learning by confusion (to reproduce, go to the Notebook A3 from the [school GitHub](https://github.com/Shmoo137/SummerSchool2021_MLinQuantum)) - R. Koch 12 | - 3.7 Pediction-based method (to reproduce, go to the Notebook A3 from the [school GitHub](https://github.com/Shmoo137/SummerSchool2021_MLinQuantum)) - R. Koch 13 | - 4.1 Toy example of a labeled two-dimensional data set - A. Gresch 14 | - 4.3 The kernel form makes a difference - A. Gresch 15 | - 4.6 Selection of new candidate points via BO using Upper Confidence Bound acquisition function - K. Nicoli 16 | - 4.7 Search for the optimal kernel - A. Dauphin 17 | - 6.4a Parameter update for a random walker - B. Requena 18 | - 7.4 Inverse Schrödinger problem solved using dP - J. Arnold 19 | - 8.4 Perceptron capacity by Cover - M. Gabrie 20 | - 8.11 Illustration of a quantum circuit (only pdf) - P. Stornati 21 | - 8.16 Variational quantum simulation - P. Stornati 22 | 23 | ### List of the figures by [FESIDO Studio Graficzne](https://fesido.pl/) in folder `graphical_designer`: 24 | - 1.1 Traditional programming vs ML 25 | - 1.2 AI vs ML vs DL 26 | - 1.4 Interplay between AI, quantum computing, many-body physics, and quantum chemistry 27 | - 1.5 Contents of these Lecture Notes 28 | - 1.6 Tree of dependencies between chapters (added in v2) 29 | - 2.2 Learing rate as a hyperparameter 30 | - 2.3 Under- and overfitting 31 | - 2.4 The bias-variance trade-off 32 | - 2.5 Geometric construction of SVMs 33 | - 2.6 Neural network (modified in v2) 34 | - 2.7 Convolutional filter 35 | - 2.8 Autoencoder 36 | - 2.9 Recurrent neural network 37 | - 2.10 Backpropagation (added in v2) 38 | - 3.3a Phase classification with PCA 39 | - 3.9b Interpretation of neural networks via bottlenecks 40 | - 4.2 A linear SVM applied to non-linearly separable data 41 | - 4.4 Bayesian neural network 42 | - 4.5 Bayesian optimization 43 | - 4.8 Three main classes of problems tackled with BO and GPRs 44 | - 4.9 BO and GPRs for feedback loops 45 | - 5.1 Scheme of a restricted Boltzmann machine 46 | - 5.2 Autoregressive neural quantum state 47 | - 5.3 Recurrent neural-network architecture as a neural quantum state 48 | - 5.4 Expressive capacity of neural quantum states 49 | - 5.5 Schematic representation of the of various ansätze 50 | - 6.1 Overview of the basic reinforcement learning setting 51 | - 6.2 Short-term and long-terms rewards in reinforcement learning algorithms 52 | - 6.3 Schematic representation of the episodic and compositional memory of various 53 | projective simulation agents 54 | - 6.4b Evolution of various walker policies 55 | - 6.5 Performance of AlphaGo and AlphaGo Zero 56 | - 6.6 Reinforcement learning for quantum feedback of an optical cavity 57 | - 6.7 Reinforcement learning for circuit optimization 58 | - 6.8 Reinforcement learning for quantum error correction 59 | - 6.10 Reinforcement learning to find optimal relaxations 60 | - 7.1 Machine learning influences physics 61 | - 7.2 Standard vs differentiable programming 62 | - 7.5 Sketch of a normalizing flow (modified in v2) 63 | - 7.6 Volume transformation (added in v2) 64 | - 7.14 Illustration of the Hamiltonian learning of a one-spin system 65 | - 7.17 Automated design on experiments (added in v2) 66 | - 8.1 Physics influences machine learning 67 | - 8.2 Statistical physics toolbox for understanding machine learning theory 68 | - 8.3 Generalization error in classical and modern regimes 69 | - 8.6 Schemes of a committee machine and random feature model 70 | - 8.11 Illustration of a quantum circuit diagram 71 | - 8.12 Quantum machine learning 72 | - 8.13 Realization of the famous Shor algorithm in a real quantum computer 73 | - 8.14 Quantum support vector machine enhanced by a quantum device 74 | - 8.15 Variational optimization of quantum circuits 75 | 76 | ### Folder `tex_files` contains: 77 | - `arXiv_v1.zip` - zipped complete set of tex files and associated ones being a basis for the arXiv v1 submission (we recommend loading it with Overleaf). 78 | - `arXiv_v2.zip` - version 2. 79 | 80 | ### Moreover, folder `colors` contains: 81 | - `colors_dict.pkl` - pickled dictionary with our RGB-coded five main colors (green, purple, yellow, orange, blue) and their three shades (dark, medium, light), 82 | - `colors_1D.pkl` - the same colors in 1D array, 83 | - `colors_2D.pkl` - colors in 2D array, 84 | - Jupyter notebook that shows how to unpickle them. 85 | 86 | ### Finally, folder `fonts` contains: 87 | - set of fonts called *New Hero* used for text in plots, 88 | - Jupyter notebook that shows how to use them with Python. 89 | 90 | ## Version 2 update (22.06.2022)! 91 | - We wrote a new section 2.5 on backpropagation in NNs (with a new fig. 2.10) 92 | - We expanded section 7.2.2 on normalizing flows (with a new fig. 7.6) 93 | - We wrote a new section 7.3.4 on automated design of experiments (with a new fig. 7.17) and expanded the outlook of 7.3 (ML for experiments). 94 | - We added the appendix C concerning kernel methods. 95 | - We added a tree of dependencies between chapters to allow the reader to choose what they want to read in a more informed way (fig. 1.6) 96 | - We modified slightly two figures: 2.6 NN and neuron and 7.5 Sketch of a normalizing flow. 97 | - We added new references following feedback from the community. -------------------------------------------------------------------------------- /colors/colors.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Shmoo137/Lecture-Notes/8403b27f5ea706c6c6972bf87e260b8c4da129d6/colors/colors.pdf -------------------------------------------------------------------------------- /colors/colors_1D.pkl: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Shmoo137/Lecture-Notes/8403b27f5ea706c6c6972bf87e260b8c4da129d6/colors/colors_1D.pkl -------------------------------------------------------------------------------- /colors/colors_2D.pkl: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Shmoo137/Lecture-Notes/8403b27f5ea706c6c6972bf87e260b8c4da129d6/colors/colors_2D.pkl -------------------------------------------------------------------------------- /colors/colors_dict.pkl: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Shmoo137/Lecture-Notes/8403b27f5ea706c6c6972bf87e260b8c4da129d6/colors/colors_dict.pkl -------------------------------------------------------------------------------- /figures/fig-1.3/ML_papers_data.csv: -------------------------------------------------------------------------------- 1 | year,count,count_domain,category 2 | 2023,7471,228516,Materials Science 3 | 2022,6304,233467,Materials Science 4 | 2021,4318,230286,Materials Science 5 | 2020,2658,203576,Materials Science 6 | 2019,1493,194057,Materials Science 7 | 2018,580,166627,Materials Science 8 | 2017,296,153440,Materials Science 9 | 2016,158,139903,Materials Science 10 | 2015,136,132434,Materials Science 11 | 2014,85,124584,Materials Science 12 | 2013,70,114146,Materials Science 13 | 2012,41,104673,Materials Science 14 | 2011,45,101384,Materials Science 15 | 2010,52,91653,Materials Science 16 | 2009,60,89826,Materials Science 17 | 2008,39,85881,Materials Science 18 | 2007,34,79470,Materials Science 19 | 2006,43,75269,Materials Science 20 | 2005,34,71462,Materials Science 21 | 2004,27,66840,Materials Science 22 | 2003,19,58725,Materials Science 23 | 2002,13,57323,Materials Science 24 | 2001,23,56278,Materials Science 25 | 2000,13,53243,Materials Science 26 | 2023,10134,309230,Chemistry 27 | 2022,8894,316218,Chemistry 28 | 2021,6742,314492,Chemistry 29 | 2020,4744,284783,Chemistry 30 | 2019,3232,288625,Chemistry 31 | 2018,2068,260668,Chemistry 32 | 2017,1413,246888,Chemistry 33 | 2016,1137,236178,Chemistry 34 | 2015,1123,229790,Chemistry 35 | 2014,1001,223942,Chemistry 36 | 2013,1100,209306,Chemistry 37 | 2012,1086,200230,Chemistry 38 | 2011,1014,193768,Chemistry 39 | 2010,945,174564,Chemistry 40 | 2009,988,171698,Chemistry 41 | 2008,930,166880,Chemistry 42 | 2007,797,158207,Chemistry 43 | 2006,756,154022,Chemistry 44 | 2005,700,144661,Chemistry 45 | 2004,651,134624,Chemistry 46 | 2003,534,125823,Chemistry 47 | 2002,456,120836,Chemistry 48 | 2001,398,116149,Chemistry 49 | 2000,329,113923,Chemistry 50 | 2023,8023,188033,Physics 51 | 2022,6943,196424,Physics 52 | 2021,5182,195694,Physics 53 | 2020,3478,182023,Physics 54 | 2019,1869,170552,Physics 55 | 2018,871,160567,Physics 56 | 2017,432,153101,Physics 57 | 2016,245,148556,Physics 58 | 2015,177,145994,Physics 59 | 2014,108,145470,Physics 60 | 2013,107,143514,Physics 61 | 2012,76,137276,Physics 62 | 2011,85,136979,Physics 63 | 2010,58,129045,Physics 64 | 2009,69,128727,Physics 65 | 2008,53,128990,Physics 66 | 2007,49,123521,Physics 67 | 2006,44,121303,Physics 68 | 2005,40,115278,Physics 69 | 2004,29,109419,Physics 70 | 2003,19,103167,Physics 71 | 2002,22,100193,Physics 72 | 2001,12,94801,Physics 73 | 2000,17,93882,Physics 74 | 2021,24325,116534,Computer Science 75 | 2020,18148,115165,Computer Science 76 | 2019,11113,101889,Computer Science 77 | 2018,6417,83167,Computer Science 78 | 2017,4079,74785,Computer Science 79 | 2016,3120,70858,Computer Science 80 | 2015,2479,64095,Computer Science 81 | 2014,2259,62505,Computer Science 82 | 2013,1940,59506,Computer Science 83 | 2012,1816,55311,Computer Science 84 | 2011,1772,53886,Computer Science 85 | 2010,1525,50167,Computer Science 86 | 2009,1485,48991,Computer Science 87 | 2008,1388,43633,Computer Science 88 | 2007,1156,38458,Computer Science 89 | 2006,1420,54709,Computer Science 90 | 2005,1279,54892,Computer Science 91 | 2004,1034,44902,Computer Science 92 | 2003,911,39042,Computer Science 93 | 2002,685,30689,Computer Science 94 | 2001,639,26615,Computer Science 95 | 2000,545,28392,Computer Science 96 | 2021,8684,33384,"Computer Science, Artificial Intelligence" 97 | 2020,6197,28679,"Computer Science, Artificial Intelligence" 98 | 2019,3745,23374,"Computer Science, Artificial Intelligence" 99 | 2018,2472,20518,"Computer Science, Artificial Intelligence" 100 | 2017,1764,19094,"Computer Science, Artificial Intelligence" 101 | 2016,1397,18856,"Computer Science, Artificial Intelligence" 102 | 2015,1109,16441,"Computer Science, Artificial Intelligence" 103 | 2014,1028,15930,"Computer Science, Artificial Intelligence" 104 | 2013,864,13949,"Computer Science, Artificial Intelligence" 105 | 2012,861,14389,"Computer Science, Artificial Intelligence" 106 | 2011,818,13956,"Computer Science, Artificial Intelligence" 107 | 2010,711,12801,"Computer Science, Artificial Intelligence" 108 | 2009,687,12042,"Computer Science, Artificial Intelligence" 109 | 2008,598,10089,"Computer Science, Artificial Intelligence" 110 | 2007,469,8537,"Computer Science, Artificial Intelligence" 111 | 2006,725,17533,"Computer Science, Artificial Intelligence" 112 | 2005,650,16959,"Computer Science, Artificial Intelligence" 113 | 2004,505,12632,"Computer Science, Artificial Intelligence" 114 | 2003,404,10668,"Computer Science, Artificial Intelligence" 115 | 2002,319,8295,"Computer Science, Artificial Intelligence" 116 | 2001,251,5634,"Computer Science, Artificial Intelligence" 117 | 2000,247,5790,"Computer Science, Artificial Intelligence" 118 | 2022,2328,34490,"Geosciences, Multidisciplinary" 119 | 2021,2209,42622,"Geosciences, Multidisciplinary" 120 | 2020,1419,40846,"Geosciences, Multidisciplinary" 121 | 2019,761,35994,"Geosciences, Multidisciplinary" 122 | 2018,364,32215,"Geosciences, Multidisciplinary" 123 | 2017,176,27616,"Geosciences, Multidisciplinary" 124 | 2016,108,28002,"Geosciences, Multidisciplinary" 125 | 2015,69,26893,"Geosciences, Multidisciplinary" 126 | 2014,51,24736,"Geosciences, Multidisciplinary" 127 | 2013,29,23758,"Geosciences, Multidisciplinary" 128 | 2012,24,20864,"Geosciences, Multidisciplinary" 129 | 2011,27,19961,"Geosciences, Multidisciplinary" 130 | 2010,11,19207,"Geosciences, Multidisciplinary" 131 | 2009,13,18527,"Geosciences, Multidisciplinary" 132 | 2008,17,17754,"Geosciences, Multidisciplinary" 133 | 2007,4,16720,"Geosciences, Multidisciplinary" 134 | 2006,7,16079,"Geosciences, Multidisciplinary" 135 | 2005,3,14476,"Geosciences, Multidisciplinary" 136 | 2004,2,13345,"Geosciences, Multidisciplinary" 137 | 2003,0,12224,"Geosciences, Multidisciplinary" 138 | 2002,3,11638,"Geosciences, Multidisciplinary" 139 | 2001,4,11351,"Geosciences, Multidisciplinary" 140 | 2000,0,10578,"Geosciences, Multidisciplinary" -------------------------------------------------------------------------------- /figures/fig-1.3/fig1-3.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Shmoo137/Lecture-Notes/8403b27f5ea706c6c6972bf87e260b8c4da129d6/figures/fig-1.3/fig1-3.pdf -------------------------------------------------------------------------------- /figures/fig-2.1/fig-2.1.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Shmoo137/Lecture-Notes/8403b27f5ea706c6c6972bf87e260b8c4da129d6/figures/fig-2.1/fig-2.1.pdf -------------------------------------------------------------------------------- /figures/fig-4.1/4.1-title.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "source": [ 6 | "# Toy example of a two-dimensional data set in the input and feature space (fig. 4.1)\r\n", 7 | "## Author: Alexander Gresch" 8 | ], 9 | "metadata": {} 10 | }, 11 | { 12 | "cell_type": "code", 13 | "execution_count": 15, 14 | "source": [ 15 | "%matplotlib inline\r\n", 16 | "import matplotlib.pyplot as plt\r\n", 17 | "import numpy as np\r\n", 18 | "import seaborn as sns" 19 | ], 20 | "outputs": [], 21 | "metadata": {} 22 | }, 23 | { 24 | "cell_type": "code", 25 | "execution_count": 16, 26 | "source": [ 27 | "path_to_colors = \"../colors/\"" 28 | ], 29 | "outputs": [], 30 | "metadata": {} 31 | }, 32 | { 33 | "cell_type": "code", 34 | "execution_count": 17, 35 | "source": [ 36 | "# Import custom font\r\n", 37 | "import matplotlib.font_manager as fm\r\n", 38 | "from pathlib import Path\r\n", 39 | "path = Path(r'../fonts/Hero New Regular.otf') # for text only\r\n", 40 | "\r\n", 41 | "path_abc = Path(r'../fonts/Hero New Medium.otf') # for (a), (b), etc.\r\n", 42 | "custom_font = fm.FontProperties(fname=path)\r\n", 43 | "custom_font_abc = fm.FontProperties(fname=path_abc)" 44 | ], 45 | "outputs": [], 46 | "metadata": {} 47 | }, 48 | { 49 | "cell_type": "code", 50 | "execution_count": 18, 51 | "source": [ 52 | "# Import colors (e.g., as 1D and dictionary)\r\n", 53 | "import pickle\r\n", 54 | "\r\n", 55 | "# Use colors as a dictionary\r\n", 56 | "infile = open(path_to_colors+'colors_dict.pkl','rb')\r\n", 57 | "colors_dict = pickle.load(infile)\r\n", 58 | "infile.close()\r\n", 59 | "\r\n", 60 | "# Import 1D array of colors\r\n", 61 | "infile = open(path_to_colors+'colors_1D.pkl','rb')\r\n", 62 | "colors_1D = pickle.load(infile)\r\n", 63 | "infile.close()" 64 | ], 65 | "outputs": [], 66 | "metadata": {} 67 | }, 68 | { 69 | "cell_type": "code", 70 | "execution_count": 19, 71 | "source": [ 72 | "# derandomized data generation\r\n", 73 | "N = 100\r\n", 74 | "r = np.random.RandomState(42)\r\n", 75 | "radii = np.zeros((2,N))\r\n", 76 | "for i,rrange in enumerate(([0.1,0.8],[1.2,1.9])):\r\n", 77 | " radii[i] = r.uniform(*rrange,size=N)\r\n", 78 | "angles = r.uniform(0,2*np.pi,size=(2,N))" 79 | ], 80 | "outputs": [], 81 | "metadata": {} 82 | }, 83 | { 84 | "cell_type": "code", 85 | "execution_count": 22, 86 | "source": [ 87 | "# Seaborn style set\r\n", 88 | "sns.set(style=\"whitegrid\", rc={'figure.figsize':(8,6)}) # in inches\r\n", 89 | "sns.set_style(\"whitegrid\", {'grid.linestyle': 'dashed', \"grid.color\": \"0.5\", 'axes.edgecolor': '.1'})\r\n", 90 | "\r\n", 91 | "ax = plt.subplot(121)\r\n", 92 | "for color,radius,angle in zip(colors_dict.values(),radii,angles):\r\n", 93 | " plt.scatter(radius*np.cos(angle),radius*np.sin(angle),c=color[\"dark\"])\r\n", 94 | "angle = np.linspace(0,2*np.pi,100)\r\n", 95 | "plt.plot(np.cos(angle),np.sin(angle),\"k-\")\r\n", 96 | "plt.xlim(-2,2)\r\n", 97 | "plt.xticks(fontsize=11)\r\n", 98 | "plt.xlabel(\"$x$\",fontsize=16,fontproperties=custom_font)\r\n", 99 | "#plt.xticks([])\r\n", 100 | "plt.ylim(-2,2)\r\n", 101 | "plt.yticks(fontsize=11)\r\n", 102 | "plt.ylabel(\"$y$\",fontsize=16,fontproperties=custom_font,rotation=0,ha=\"right\")\r\n", 103 | "plt.text(0.8,0.875,\"(a)\",fontsize=16,fontproperties=custom_font_abc,transform=ax.transAxes)\r\n", 104 | "plt.yticks([-2,-1,0,1,2])\r\n", 105 | "plt.gca().set_aspect('equal')\r\n", 106 | "\r\n", 107 | "ax.set_yticklabels(ax.get_yticks().astype('int'), font=path, fontsize=12)\r\n", 108 | "ax.set_xticklabels(ax.get_xticks().astype('int'), font=path, fontsize=12)\r\n", 109 | "\r\n", 110 | "ax = plt.subplot(122)\r\n", 111 | "for color,radius,angle in zip(colors_dict.values(),radii**2,angles):\r\n", 112 | " plt.scatter(radius*np.cos(angle)**2,radius*np.sin(angle)**2,c=color[\"dark\"])\r\n", 113 | "plt.plot([-1,2],[2,-1],\"k-\")\r\n", 114 | "#plt.xlim(-0.1,1.9**2+0.1)\r\n", 115 | "plt.xlim(0,3.9)\r\n", 116 | "plt.xticks(fontsize=11)\r\n", 117 | "plt.xlabel(\"$x^2$\",fontsize=16,fontproperties=custom_font)\r\n", 118 | "#plt.xticks([])\r\n", 119 | "#plt.ylim(-0.1,1.9**2+0.1)\r\n", 120 | "plt.ylim(0,3.9)\r\n", 121 | "plt.yticks(fontsize=11)\r\n", 122 | "plt.ylabel(\"$y^2$\",fontsize=16,fontproperties=custom_font,rotation=0,ha=\"right\") #,labelpad=-245\r\n", 123 | "plt.text(0.8,0.875,\"(b)\",fontsize=16,fontproperties=custom_font_abc,transform=ax.transAxes)\r\n", 124 | "plt.yticks([0,1,2,3])\r\n", 125 | "\r\n", 126 | "ax.set_yticklabels(ax.get_yticks().astype('int'), font=path, fontsize=12)\r\n", 127 | "ax.set_xticklabels(ax.get_xticks().astype('int'), font=path, fontsize=12)\r\n", 128 | "\r\n", 129 | "plt.gca().set_aspect('equal')\r\n", 130 | "plt.subplots_adjust(wspace=0.3)\r\n", 131 | "plt.savefig(\"4.1_toy_example_corr.pdf\",orientation=\"landscape\",dpi=600,bbox_inches=\"tight\")" 132 | ], 133 | "outputs": [ 134 | { 135 | "output_type": "stream", 136 | "name": "stderr", 137 | "text": [ 138 | "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*. Please use the *color* keyword-argument or provide a 2-D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n", 139 | "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*. Please use the *color* keyword-argument or provide a 2-D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n", 140 | "C:\\Users\\ankad\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:22: UserWarning: FixedFormatter should only be used together with FixedLocator\n", 141 | "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*. Please use the *color* keyword-argument or provide a 2-D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n", 142 | "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*. Please use the *color* keyword-argument or provide a 2-D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n", 143 | "C:\\Users\\ankad\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:41: UserWarning: FixedFormatter should only be used together with FixedLocator\n", 144 | "'Hero New Medium.otf' can not be subsetted into a Type 3 font. The entire font will be embedded in the output.\n", 145 | "'Hero New Regular.otf' can not be subsetted into a Type 3 font. The entire font will be embedded in the output.\n" 146 | ] 147 | }, 148 | { 149 | "output_type": "display_data", 150 | "data": { 151 | "image/png": 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", 152 | "text/plain": [ 153 | "
" 154 | ] 155 | }, 156 | "metadata": {} 157 | } 158 | ], 159 | "metadata": {} 160 | }, 161 | { 162 | "cell_type": "code", 163 | "execution_count": null, 164 | "source": [], 165 | "outputs": [], 166 | "metadata": {} 167 | } 168 | ], 169 | "metadata": { 170 | "interpreter": { 171 | "hash": "fcb4468fb47c6127ab44332c3f3439a85914e2850b2efd86c12e06a03080f93f" 172 | }, 173 | "kernelspec": { 174 | "name": "python3", 175 | "display_name": "Python 3.7.4 64-bit ('base': conda)" 176 | }, 177 | "language_info": { 178 | "codemirror_mode": { 179 | "name": "ipython", 180 | "version": 3 181 | }, 182 | "file_extension": ".py", 183 | "mimetype": "text/x-python", 184 | "name": "python", 185 | "nbconvert_exporter": "python", 186 | "pygments_lexer": "ipython3", 187 | "version": "3.7.4" 188 | } 189 | }, 190 | "nbformat": 4, 191 | "nbformat_minor": 4 192 | } -------------------------------------------------------------------------------- /figures/fig-4.1/4.1_toy_example_corr.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Shmoo137/Lecture-Notes/8403b27f5ea706c6c6972bf87e260b8c4da129d6/figures/fig-4.1/4.1_toy_example_corr.pdf -------------------------------------------------------------------------------- /figures/fig-4.2/4.2_kernel_trick_comparison_corr.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Shmoo137/Lecture-Notes/8403b27f5ea706c6c6972bf87e260b8c4da129d6/figures/fig-4.2/4.2_kernel_trick_comparison_corr.pdf -------------------------------------------------------------------------------- /figures/fig-4.6/figure4.6.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Shmoo137/Lecture-Notes/8403b27f5ea706c6c6972bf87e260b8c4da129d6/figures/fig-4.6/figure4.6.pdf -------------------------------------------------------------------------------- /figures/fig-4.7/4.7-kernel-search-Alexandre-Dauphin.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Shmoo137/Lecture-Notes/8403b27f5ea706c6c6972bf87e260b8c4da129d6/figures/fig-4.7/4.7-kernel-search-Alexandre-Dauphin.pdf -------------------------------------------------------------------------------- /figures/fig-6.4/6.4-title.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "source": [ 6 | "# Title of the figure (fig. 1.3)\n", 7 | "## Author:Borja Requena " 8 | ], 9 | "metadata": {} 10 | }, 11 | { 12 | "cell_type": "code", 13 | "execution_count": 2, 14 | "source": [ 15 | "%matplotlib inline\r\n", 16 | "import matplotlib.pyplot as plt\r\n", 17 | "import numpy as np\r\n", 18 | "import seaborn as sns\r\n", 19 | "from pathlib import Path" 20 | ], 21 | "outputs": [], 22 | "metadata": {} 23 | }, 24 | { 25 | "cell_type": "code", 26 | "execution_count": 3, 27 | "source": [ 28 | "# Import custom font\r\n", 29 | "import matplotlib.font_manager as fm\r\n", 30 | "\r\n", 31 | "path = Path(r'../fonts/Hero New Regular.otf') # for text only\r\n", 32 | "path_abc = r'../fonts/Hero New Medium.otf' # for (a), (b), etc.\r\n", 33 | "custom_font = fm.FontProperties(fname=path)\r\n", 34 | "custom_font_abc = fm.FontProperties(fname=path_abc)" 35 | ], 36 | "outputs": [], 37 | "metadata": {} 38 | }, 39 | { 40 | "cell_type": "code", 41 | "execution_count": 4, 42 | "source": [ 43 | "# Import colors (e.g., as 1D and dictionary)\r\n", 44 | "import pickle\r\n", 45 | "\r\n", 46 | "# Use colors as a dictionary\r\n", 47 | "infile = open(Path('../colors/colors_dict.pkl'),'rb')\r\n", 48 | "colors_dict = pickle.load(infile)\r\n", 49 | "infile.close()\r\n", 50 | "\r\n", 51 | "# Import 1D array of colors\r\n", 52 | "infile = open(Path('../colors/colors_1D.pkl'),'rb')\r\n", 53 | "colors_1D = pickle.load(infile)\r\n", 54 | "infile.close()" 55 | ], 56 | "outputs": [], 57 | "metadata": {} 58 | }, 59 | { 60 | "cell_type": "code", 61 | "execution_count": 5, 62 | "source": [ 63 | "x = np.linspace(0, 1, 1000)\r\n", 64 | "y = x*(1-x)" 65 | ], 66 | "outputs": [], 67 | "metadata": {} 68 | }, 69 | { 70 | "cell_type": "code", 71 | "execution_count": 60, 72 | "source": [ 73 | "# Seaborn style set\r\n", 74 | "sns.set(style=\"whitegrid\", rc={'figure.figsize':(4,3.375)}) # in inches\r\n", 75 | "sns.set_style(\"whitegrid\", {\"grid.linestyle\": 'dashed', \r\n", 76 | " \"grid.color\": colors_dict[\"blue\"][\"light\"], \r\n", 77 | " \"axes.edgecolor\": colors_dict[\"blue\"][\"dark\"],\r\n", 78 | " \"axes.labelcolor\": colors_dict[\"blue\"][\"dark\"],\r\n", 79 | " 'xtick.color': colors_dict[\"blue\"][\"dark\"],\r\n", 80 | " 'ytick.color': colors_dict[\"blue\"][\"dark\"],\r\n", 81 | " })\r\n", 82 | "\r\n", 83 | "fig, ax = plt.subplots()\r\n", 84 | "\r\n", 85 | "ax.plot(x, y, color=colors_dict[\"purple\"][\"dark\"])\r\n", 86 | "ax.set_xlabel(r'$\\pi_\\theta($up$)$', size=12, fontproperties=custom_font)\r\n", 87 | "ax.set_ylabel(r'$\\Delta\\theta\\propto\\pi_\\theta($up$)(1-\\pi_\\theta($up$))$', size=12, fontproperties=custom_font)\r\n", 88 | "ax.tick_params(labelsize=10)\r\n", 89 | "plt.tight_layout()\r\n", 90 | "ax.axis([-0.05, 1.05, -0.02, 0.32])\r\n", 91 | "\r\n", 92 | "yticks = [0,0.1,0.2,0.3]\r\n", 93 | "xticks = np.arange(0.,1.1,0.25)\r\n", 94 | "ax.set_yticks(yticks)\r\n", 95 | "ax.set_xticks(xticks)\r\n", 96 | "\r\n", 97 | "ax.set_yticklabels(ax.get_yticks(), font=path, fontsize=12)\r\n", 98 | "ax.set_xticklabels(ax.get_xticks(), font=path, fontsize=12)\r\n", 99 | "\r\n", 100 | "#plt.show()\r\n", 101 | "plt.savefig('fig6-4.pdf')" 102 | ], 103 | "outputs": [ 104 | { 105 | "output_type": "stream", 106 | "name": "stderr", 107 | "text": [ 108 | "'Hero New Regular.otf' can not be subsetted into a Type 3 font. The entire font will be embedded in the output.\n" 109 | ] 110 | }, 111 | { 112 | "output_type": "display_data", 113 | "data": { 114 | "image/png": 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", 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" 117 | ] 118 | }, 119 | "metadata": {} 120 | } 121 | ], 122 | "metadata": {} 123 | }, 124 | { 125 | "cell_type": "code", 126 | "execution_count": null, 127 | "source": [], 128 | "outputs": [], 129 | "metadata": {} 130 | } 131 | ], 132 | "metadata": { 133 | "interpreter": { 134 | "hash": "fcb4468fb47c6127ab44332c3f3439a85914e2850b2efd86c12e06a03080f93f" 135 | }, 136 | "kernelspec": { 137 | "name": "python3", 138 | "display_name": "Python 3.7.4 64-bit ('base': conda)" 139 | }, 140 | "language_info": { 141 | "codemirror_mode": { 142 | "name": "ipython", 143 | "version": 3 144 | }, 145 | "file_extension": ".py", 146 | "mimetype": "text/x-python", 147 | "name": "python", 148 | "nbconvert_exporter": "python", 149 | "pygments_lexer": "ipython3", 150 | "version": "3.7.4" 151 | } 152 | }, 153 | "nbformat": 4, 154 | "nbformat_minor": 4 155 | } -------------------------------------------------------------------------------- /figures/fig-6.4/fig-6.4b_walker_convergence.pdf: 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/figures/fig-8.11/quantum_circuit_with_measurement.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Shmoo137/Lecture-Notes/8403b27f5ea706c6c6972bf87e260b8c4da129d6/figures/fig-8.11/quantum_circuit_with_measurement.pdf -------------------------------------------------------------------------------- /figures/fig-8.16/8.16b-optimization.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "source": [ 6 | "# Plot of the energy of the system during the optimization (fig. 8.16b)\r\n", 7 | "## Author: Paolo Stornati" 8 | ], 9 | "metadata": {} 10 | }, 11 | { 12 | "cell_type": "code", 13 | "execution_count": 1, 14 | "source": [ 15 | "%matplotlib inline\r\n", 16 | "import matplotlib.pyplot as plt\r\n", 17 | "import numpy as np\r\n", 18 | "import seaborn as sns" 19 | ], 20 | "outputs": [], 21 | "metadata": {} 22 | }, 23 | { 24 | "cell_type": "code", 25 | "execution_count": 31, 26 | "source": [ 27 | "# Import custom font\r\n", 28 | "import matplotlib.font_manager as fm\r\n", 29 | "\r\n", 30 | "path = r'../fonts/Hero New Regular.otf' # for text only\r\n", 31 | "#path = r'your-path\\fonts\\Hero New Medium.otf' # for (a), (b), etc.\r\n", 32 | "custom_font = fm.FontProperties(fname=path)" 33 | ], 34 | "outputs": [], 35 | "metadata": {} 36 | }, 37 | { 38 | "cell_type": "code", 39 | "execution_count": 32, 40 | "source": [ 41 | "# Import colors (e.g., as 1D and dictionary)\r\n", 42 | "import pickle\r\n", 43 | "\r\n", 44 | "# Use colors as a dictionary\r\n", 45 | "infile = open('../colors/colors_dict.pkl','rb')\r\n", 46 | "colors_dict = pickle.load(infile)\r\n", 47 | "infile.close()\r\n", 48 | "\r\n", 49 | "# Import 1D array of colors\r\n", 50 | "infile = open('/Users/paolostornati/ICFO/Lecture-Notes/colors/colors_1D.pkl','rb')\r\n", 51 | "colors_1D = pickle.load(infile)\r\n", 52 | "infile.close()\r\n" 53 | ], 54 | "outputs": [], 55 | "metadata": {} 56 | }, 57 | { 58 | "cell_type": "code", 59 | "execution_count": 33, 60 | "source": [ 61 | "from tempfile import TemporaryFile\r\n", 62 | "with open('data_cutted.npy', 'rb') as f:\r\n", 63 | " a = np.load(f)\r\n", 64 | " b = np.load(f)\r\n", 65 | "print(a,b)" 66 | ], 67 | "outputs": [ 68 | { 69 | "output_type": "stream", 70 | "name": "stdout", 71 | "text": [ 72 | "[ -2.52518585 -2.52518585 -2.52518585 -2.52518585 -4.6885406\n", 73 | " -2.68446488 -2.35926105 -2.65041467 -2.47137099 -2.51351389\n", 74 | " -2.53126515 -1.99679623 -4.17944413 -2.5127993 -2.80484788\n", 75 | " -2.8383992 -3.5274279 -3.44394031 -3.42223314 -3.51867986\n", 76 | " -3.19811664 -3.71812856 -3.86394205 -4.02651191 -4.20729678\n", 77 | " -4.4076696 -4.62597571 -4.73577928 -5.61255073 -4.95031745\n", 78 | " -5.08195865 -5.47391872 -5.05219342 -5.41888094 -5.39115471\n", 79 | " -5.68247408 -6.40615246 -6.14557567 -6.12906049 -6.2387777\n", 80 | " -6.32748377 -6.48508163 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-16.76314918 -16.76345002 -16.76685699 -16.76593342\n", 186 | " -16.76802251 -16.76885751 -16.76783798 -16.76509561 -16.76596078\n", 187 | " -16.76537047 -16.76547862 -16.76743538 -16.76939145 -16.76996405\n", 188 | " -16.76931448 -16.77072326 -16.77204357 -16.77011485 -16.76980013\n", 189 | " -16.76845827 -16.76909444 -16.77043669 -16.77152645 -16.77451094\n", 190 | " -16.77785054 -16.77259589 -16.77208332 -16.77610038 -16.77488745\n", 191 | " -16.77603106 -16.77476579 -16.77410459 -16.77616377] [-16.92631169]\n" 192 | ] 193 | } 194 | ], 195 | "metadata": {} 196 | }, 197 | { 198 | "cell_type": "code", 199 | "execution_count": 40, 200 | "source": [ 201 | "# Seaborn style set\n", 202 | "sns.set(style=\"whitegrid\", rc={'figure.figsize':(5.25,3.375)}) # in inches\n", 203 | "sns.set_style(\"whitegrid\", {'grid.linestyle': 'dashed', \"grid.color\": \"0.5\", 'axes.edgecolor': '.1'})\n", 204 | "\n", 205 | "plt.plot(a,color=colors_dict[\"blue\"][\"dark\"], label=r'Energy')\n", 206 | "plt.hlines(y=b[0],color=colors_dict[\"orange\"][\"dark\"], xmin=0, xmax=len(a), label=r'Exact Solution')\n", 207 | "plt.xlabel(r'$Optimization \\, Step$', size=12, fontproperties=custom_font)\n", 208 | "plt.ylabel(r'$Energy$', size=12, fontproperties=custom_font)\n", 209 | "plt.tick_params(labelsize=10)\n", 210 | "plt.tight_layout()\n", 211 | "plt.legend(fontsize = 10, framealpha = 0.9)\n", 212 | "#plt.axis([1.0, 3.0, -0.05, 1.05])\n", 213 | "#plt.yticks(np.arange(0,1.1,0.25))\n", 214 | "#plt.xticks(np.arange(1,3.1,0.5))\n", 215 | "\n", 216 | "#plt.show()\n", 217 | "plt.savefig('sth.pdf')" 218 | ], 219 | "outputs": [ 220 | { 221 | "output_type": "display_data", 222 | "data": { 223 | "image/png": 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", 224 | "text/plain": [ 225 | "
" 226 | ] 227 | }, 228 | "metadata": {} 229 | } 230 | ], 231 | "metadata": {} 232 | }, 233 | { 234 | "cell_type": "code", 235 | "execution_count": 23, 236 | "source": [ 237 | "plt.plot(a)\n", 238 | "plt.hlines(y=b[0],xmin=0, xmax=len(a))" 239 | ], 240 | "outputs": [ 241 | { 242 | "output_type": "execute_result", 243 | "data": { 244 | "text/plain": [ 245 | "" 246 | ] 247 | }, 248 | "metadata": {}, 249 | "execution_count": 23 250 | }, 251 | { 252 | "output_type": "display_data", 253 | "data": { 254 | "image/png": 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", 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"cell_type": "code", 18 | "execution_count": 3, 19 | "source": [ 20 | "![image](font_examples.png \"Font examples\")" 21 | ], 22 | "outputs": [ 23 | { 24 | "output_type": "stream", 25 | "name": "stderr", 26 | "text": [ 27 | "'[image]' is not recognized as an internal or external command,\n", 28 | "operable program or batch file.\n" 29 | ] 30 | } 31 | ], 32 | "metadata": {} 33 | }, 34 | { 35 | "cell_type": "code", 36 | "execution_count": 4, 37 | "source": [ 38 | "import numpy as np\r\n", 39 | "\r\n", 40 | "# Data for plotting\r\n", 41 | "t = np.arange(0.0, 2.0, 0.01)\r\n", 42 | "s = 1 + np.sin(2 * np.pi * t)\r\n", 43 | "\r\n", 44 | "fig, axs = plt.subplots(1,2)\r\n", 45 | "\r\n", 46 | "i=0\r\n", 47 | "for ax in axs:\r\n", 48 | " ax.plot(t, s+i)\r\n", 49 | " i+=2\r\n", 50 | " ax.grid()\r\n", 51 | " ax.set_ylim(0,4)\r\n", 52 | "\r\n", 53 | "axs[0].text(1.5, 3.5, \"(a)\", fontsize=12, fontproperties=custom_font)\r\n", 54 | "axs[1].text(1.5, 0.5, \"(b)\", fontsize=12, fontproperties=custom_font)\r\n", 55 | "\r\n", 56 | "plt.show()" 57 | ], 58 | "outputs": [ 59 | { 60 | "output_type": "display_data", 61 | "data": { 62 | "image/png": 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