├── README.md └── overview.png /README.md: -------------------------------------------------------------------------------- 1 | ## Artificial Intelligence and Machine Learning For Quantum Technologies 2 | by **[Mario Krenn](https://mpl.mpg.de/research-at-mpl/independent-research-groups/krenn-research-group/), Jonas Landgraf, Thomas Foesel, [Florian Marquardt](https://mpl.mpg.de/divisions/marquardt-division)**\ 3 | Perspective Paper: [arXiv:2208.03836 (2022)](https://arxiv.org/abs/2208.03836)\ 4 | contact: ML4qtech@mpl.mpg.de 5 | 6 |  
7 | **Mini Index**\ 8 | [I) Basics of Artificial Intelligence and Machine Learning](https://github.com/ML4QTech/Collection#basics-of-artificial-intelligence-and-machine-learning)\ 9 | [II) AI for Quantum Technology - Repos from the Community](https://github.com/ML4QTech/Collection#ai-for-quantum-technology---repos-from-the-community) 10 | 11 |  
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14 | Overview 15 |

16 | 17 | ## I) Basics of Artificial Intelligence and Machine Learning 18 | 19 | ### General resources 20 | - [Basics of Neural Networks](https://www.dkriesel.com/en/science/neural_networks): Lightweight introduction to machine learning and neural networks 21 | - [Neural Networks and Deep Learning](http://neuralnetworksanddeeplearning.com): Basic, compact and intuitive introduction to neural networks, supervised learning and the backpropagation algorithm 22 | - [Deep Learning](https://www.deeplearningbook.org/): A detailed introduction to the field of machine learning 23 | - [Dive into Deep Learning](https://d2l.ai/): An interactive deep learning book with code, math, and discussions 24 | - Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 25 | - [Reinforcement Learning: An Introduction](http://incompleteideas.net/book/the-book.html): A detailed introduction to reinforcement learning 26 | 27 | ### Resources for STEM students 28 | - [Machine Learning for Scientists](https://ml-lectures.org): see also [arXiv:2102.04883](https://arxiv.org/abs/2102.04883) 29 | - [A high-bias, low-variance introduction to Machine Learning for physicists](https://arxiv.org/abs/1803.08823) 30 | - Machine Learning for Physicists lecture by Florian Marquardt: [basic](https://machine-learning-for-physicists.org/) and [advanced lectures](https://pad.gwdg.de/2021_AdvancedMachineLearningForScience) 31 | 32 | ### Machine Learning Libraries 33 | 34 | - [Tensorflow](https://www.tensorflow.org/) 35 | - [Pytorch](https://pytorch.org/) 36 | - [Jax](https://github.com/google/jax) 37 | - [Stable Baselines](https://stable-baselines.readthedocs.io) 38 | and many more 39 | 40 | ## II) AI for Quantum Technology - Repos from the Community 41 | 42 | Here we collect repositories that demonstrate works on AI in quantum technology. If you have additonal suggestions, please make a PR or send to [ML4QTech@mpl.mpg.de](ML4QTech@mpl.mpg.de) 43 | 44 | ### 1) Measurement data analysis and quantum state representation 45 | #### Interpreting Measurements 46 | - [**Unsupervised Phase Discovery with Deep Anomaly Detection**](https://github.com/Qottmann/phase-discovery-anomaly-detection) by Kottmann, Huembeli, Lewenstein, Acín ([paper](https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.125.170603)) 47 | 48 | #### Approximation of Quantum States 49 | - [**Quantum state tomography with conditional genreative adversarial networks**](https://github.com/quantshah/qst-cgan) by Ahmed, Muñoz, Nori, Frisk Kockum ([paper](https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.127.140502)) 50 | - [**Gradient-descent quantum process tomography by learning Kraus operators**](https://github.com/quantshah/gd-qpt) by Ahmed, Quijandría, Frisk Kockum ([paper](https://arxiv.org/abs/2208.00812)) 51 | - [**Recurrent neural network wave functions**](https://github.com/mhibatallah/RNNWavefunctions) by Hibat-Allah, Ganahl, Hayward, Melko, Carrasquilla ([paper](https://journals.aps.org/prresearch/abstract/10.1103/PhysRevResearch.2.023358)) 52 | - [**NetKet 3: Machine Learning Toolbox for Many-Body Quantum Systems**](https://github.com/netket/netket) by Vicentini, Hofmann, Szabó, Wu, Roth, Giuliani, Pescia, Nys, Vargas-Calderon, Astrakhantsev, Carleo ([paper](https://arxiv.org/abs/2112.10526)) 53 | 54 | #### Approximating Quantum Dynamics 55 | - [**Learning quantum dynamics with latent neural ordinary differential equations**](https://github.com/aspuru-guzik-group/QNODE) by Choi, Flam-Spepherd, Kyaw, Aspuru-Guzik ([paper](https://journals.aps.org/pra/abstract/10.1103/PhysRevA.105.042403)) 56 | - [**Time-Dependent Variational Principle for Open Quantum Systems with Artificial Neural Networks**](https://github.com/markusschmitt/vmc_jax/tree/master/jVMC) by Reh, Schmitt, Gärttner ([paper](https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.127.230501)) 57 | 58 | ### 2) Parameter estimation: learning the properties of a quantum system 59 | #### Quantum Metrology 60 | - [**Generalizable control for quantum parameter estimation through reinforcement learning**](https://github.com/MilCOS/Quantum_Parameter_Estimation_with_RL) by Xu, Li, Liu, Wang, Yuan, Wang ([paper](https://www.nature.com/articles/s41534-019-0198-z)) 61 | #### Device calibration 62 | - [**Efficiently measuring a quantum device using machine learning**](https://github.com/returnddd/CVAE_for_QE) by Lennon, Moon, Camenzind, Yu, Zumbühl, Briggs, Osborne, Laird, Ares ([paper](https://www.nature.com/articles/s41534-019-0193-4)) 63 | 64 | #### Quantum Hamiltonian Learning 65 | - [**Learning models of quantum systems from experiments**](https://github.com/flynnbr11/QMLA) by Gentile, Flynn, Knauer, Wiebe, Paesani, Granade, Rarity, Santagati, Laing ([paper](https://www.nature.com/articles/s41567-021-01201-7)) 66 | 67 | 68 | 69 | ### 3) Discovering strategies for hardware-level quantum control 70 | 71 | #### Quantum control tasks without feedback (open-loop control) 72 | - [**Reinforcement Learning in Different Phases of Quantum Control**](https://github.com/mgbukov/dynamicQL/tree/master/SA) by Bukov, Day, Sels, Weinberg, Polkovnikov, Mehta ([paper](https://doi.org/https://doi.org/10.1103/PhysRevX.8.031086)) 73 | 74 | #### Quantum feedback control (closed-loop control) 75 | - [**Deep Reinforcement Learning Control of Quantum Cartpoles**](https://github.com/Z-T-WANG/DeepReinforcementLearningControlOfQuantumCartpoles) by Wang, Ashida, Ueda ([paper](https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.125.100401)) 76 | - [**Model-Free Quantum Control with Reinforcement Learning**](https://github.com/v-sivak/quantum-control-rl) by Sivak, Eickbusch, Royer, Tsioutsios, Devoret ([paper](https://doi.org/10.1103/PhysRevX.12.011059)) 77 | 78 | #### Model-free vs model-based RL 79 | - [**Speedup for quantum optimal control from automatic differentiation based on graphics processing units**](https://github.com/SchusterLab/quantum-optimal-control) by Leung, Abdelhafez, Koch, Schuster ([paper](https://doi.org/10.1103/PhysRevA.95.042318)) 80 | - [**A differentiable programming method for quantum control**](https://github.com/frankschae/A-differentiable-programming-method-for-quantum-control) by Schäfer, Kloc, Bruder, Lörch ([paper](https://doi.org/10.1088/2632-2153/ab9802)) 81 | - [**Protocol Discovery for the Quantum Control of Majoranas by Differentiable Programming and Natural Evolution Strategies**](https://github.com/LuukCoopmans/Majorana-Game) by Coopmans, Luo, Kells, Clark, Carrasquilla ([paper](https://doi.org/10.1103/PRXQuantum.2.020332)) 82 | - [**Control of stochastic quantum dynamics by differentiable programming**](https://github.com/frankschae/Control-of-Stochastic-Quantum-Dynamics-with-Differentiable-Programming) by Schäfer, Sekatski, Koppenhöfer, Bruder, Kloc ([paper](https://doi.org/10.1088/2632-2153/abec22)) 83 | 84 | 85 | ### 4) Discovering quantum experiments, protocols, and circuits 86 | 87 | #### Discovery of Quantum Experiments 88 | - [**Automated Search for new Quantum Experiments**](https://github.com/XuemeiGu/MelvinPython) by Krenn, Malik, Fickler, Lapkiewicz, Zeilinger, implemented by Gu ([paper](https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.116.090405)) 89 | - [**Conceptual Understanding through Efficient Automated Design of Quantum Optical Experiments**](https://github.com/aspuru-guzik-group/Theseus) by Krenn, Kottmann, Tischler, Aspuru-Guzik ([paper](https://journals.aps.org/prx/abstract/10.1103/PhysRevX.11.031044)) 90 | - [**Designing quantum experiments with a genetic algorithm**](https://github.com/paulk444/AdaQuantum) by Nichols, Mineh, Rubio, Matthews, Knott ([paper](https://iopscience.iop.org/article/10.1088/2058-9565/ab4d89)) 91 | - [**Automated design of superconducting circuits and its application to 4-local couplers**](https://github.com/aspuru-guzik-group/scilla) by Menke, Häse, Gustavsson, Kerman, Oliver, Aspuru-Guzik ([paper](https://www.nature.com/articles/s41534-021-00382-6)) 92 | 93 | #### Discovering Quantum Protocols and Discrete Feedback Strategies 94 | - [**Machine Learning for Long-Distance Quantum Communication**](https://github.com/qic-ibk/ps_quantum_comm) by Wallnöfer, Melnikov, Dür, Briegel ([paper](https://journals.aps.org/prxquantum/abstract/10.1103/PRXQuantum.1.010301)) 95 | 96 | #### Quantum Circuits 97 | - [**Quantum computer-aided design of quantum optics hardware**](https://github.com/kottmanj/Photonic) by Kottmann, Krenn, Kyaw, Alperin-Lea, Aspuru-Guzik ([paper](https://iopscience.iop.org/article/10.1088/2058-9565/abfc94/meta)) 98 | - [**Automatically differentiable circuits for chemistry**](https://github.com/tequilahub/tequila) by Kottmann, Anand, Aspuru-Guzik ([paper](https://arxiv.org/abs/2011.05938)) 99 | - [**Meta-VQEs learning quantum circuit angles from physical parameters**](https://github.com/aspuru-guzik-group/meta-VQE) by Cervera-Lierta, Kottmann, Aspuru-Guzik ([paper](https://arxiv.org/abs/2009.13545)) 100 | 101 | ### 5) Quantum Error Correction 102 | - [**Automated Discovery of Autonomous Quantum Error Correction Schemes**](https://github.com/stanfordLINQS/SQcircuit/) by Rajabzadeh, Wang, Lee, Makihara, Guo, Safavi-Naeini ([paper](https://journals.aps.org/prxquantum/abstract/10.1103/PRXQuantum.3.020302)) 103 | - [**Scalable Neural Decoder for Topological Surface Codes**](https://github.com/chaeyeunpark/UnionFind) by Meinerz, Park, Trebst ([paper](https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.128.080505)) 104 | -------------------------------------------------------------------------------- /overview.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ML4QTech/Collection/851bf42a0524c6cd04d980d54a86219971a968b1/overview.png --------------------------------------------------------------------------------