Artificial Intelligence and Machine Learning For Quantum Technologies

by Mario Krenn, Jonas Landgraf, Thomas Foesel, Florian Marquardt
Perspective Paper: arXiv:2208.03836 (2022)
contact: [email protected]

 
Mini Index
I) Basics of Artificial Intelligence and Machine Learning
II) AI for Quantum Technology - Repos from the Community

 
 

I) Basics of Artificial Intelligence and Machine Learning

General resources

• Basics of Neural Networks: Lightweight introduction to machine learning and neural networks
• Neural Networks and Deep Learning: Basic, compact and intuitive introduction to neural networks, supervised learning and the backpropagation algorithm
• Deep Learning: A detailed introduction to the field of machine learning
• Dive into Deep Learning: An interactive deep learning book with code, math, and discussions
• Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow
• Reinforcement Learning: An Introduction: A detailed introduction to reinforcement learning

Resources for STEM students

• Machine Learning for Scientists: see also arXiv:2102.04883
• A high-bias, low-variance introduction to Machine Learning for physicists
• Machine Learning for Physicists lecture by Florian Marquardt: basic and advanced lectures

Machine Learning Libraries

• Tensorflow
• Pytorch
• Jax
• Stable Baselines and many more

II) AI for Quantum Technology - Repos from the Community

Here we collect repositories that demonstrate works on AI in quantum technology. If you have additonal suggestions, please make a PR or send to [email protected]

1) Measurement data analysis and quantum state representation

Interpreting Measurements

• Unsupervised Phase Discovery with Deep Anomaly Detection by Kottmann, Huembeli, Lewenstein, Acín (paper)

Approximation of Quantum States

• Quantum state tomography with conditional genreative adversarial networks by Ahmed, Muñoz, Nori, Frisk Kockum (paper)
• Gradient-descent quantum process tomography by learning Kraus operators by Ahmed, Quijandría, Frisk Kockum (paper)
• Recurrent neural network wave functions by Hibat-Allah, Ganahl, Hayward, Melko, Carrasquilla (paper)
• NetKet 3: Machine Learning Toolbox for Many-Body Quantum Systems by Vicentini, Hofmann, Szabó, Wu, Roth, Giuliani, Pescia, Nys, Vargas-Calderon, Astrakhantsev, Carleo (paper)

Approximating Quantum Dynamics

• Learning quantum dynamics with latent neural ordinary differential equations by Choi, Flam-Spepherd, Kyaw, Aspuru-Guzik (paper)
• Time-Dependent Variational Principle for Open Quantum Systems with Artificial Neural Networks by Reh, Schmitt, Gärttner (paper)

2) Parameter estimation: learning the properties of a quantum system

Quantum Metrology

• Generalizable control for quantum parameter estimation through reinforcement learning by Xu, Li, Liu, Wang, Yuan, Wang (paper)

Device calibration

• Efficiently measuring a quantum device using machine learning by Lennon, Moon, Camenzind, Yu, Zumbühl, Briggs, Osborne, Laird, Ares (paper)

Quantum Hamiltonian Learning

• Learning models of quantum systems from experiments by Gentile, Flynn, Knauer, Wiebe, Paesani, Granade, Rarity, Santagati, Laing (paper)

3) Discovering strategies for hardware-level quantum control

Quantum control tasks without feedback (open-loop control)

• Reinforcement Learning in Different Phases of Quantum Control by Bukov, Day, Sels, Weinberg, Polkovnikov, Mehta (paper)

Quantum feedback control (closed-loop control)

• Deep Reinforcement Learning Control of Quantum Cartpoles by Wang, Ashida, Ueda (paper)
• Model-Free Quantum Control with Reinforcement Learning by Sivak, Eickbusch, Royer, Tsioutsios, Devoret (paper)

Model-free vs model-based RL

• Speedup for quantum optimal control from automatic differentiation based on graphics processing units by Leung, Abdelhafez, Koch, Schuster (paper)
• A differentiable programming method for quantum control by Schäfer, Kloc, Bruder, Lörch (paper)
• Protocol Discovery for the Quantum Control of Majoranas by Differentiable Programming and Natural Evolution Strategies by Coopmans, Luo, Kells, Clark, Carrasquilla (paper)
• Control of stochastic quantum dynamics by differentiable programming by Schäfer, Sekatski, Koppenhöfer, Bruder, Kloc (paper)

4) Discovering quantum experiments, protocols, and circuits

Discovery of Quantum Experiments

• Automated Search for new Quantum Experiments by Krenn, Malik, Fickler, Lapkiewicz, Zeilinger, implemented by Gu (paper)
• Conceptual Understanding through Efficient Automated Design of Quantum Optical Experiments by Krenn, Kottmann, Tischler, Aspuru-Guzik (paper)
• Designing quantum experiments with a genetic algorithm by Nichols, Mineh, Rubio, Matthews, Knott (paper)
• Automated design of superconducting circuits and its application to 4-local couplers by Menke, Häse, Gustavsson, Kerman, Oliver, Aspuru-Guzik (paper)

Discovering Quantum Protocols and Discrete Feedback Strategies

• Machine Learning for Long-Distance Quantum Communication by Wallnöfer, Melnikov, Dür, Briegel (paper)

Quantum Circuits

• Quantum computer-aided design of quantum optics hardware by Kottmann, Krenn, Kyaw, Alperin-Lea, Aspuru-Guzik (paper)
• Automatically differentiable circuits for chemistry by Kottmann, Anand, Aspuru-Guzik (paper)
• Meta-VQEs learning quantum circuit angles from physical parameters by Cervera-Lierta, Kottmann, Aspuru-Guzik (paper)

5) Quantum Error Correction

• Automated Discovery of Autonomous Quantum Error Correction Schemes by Rajabzadeh, Wang, Lee, Makihara, Guo, Safavi-Naeini (paper)
• Scalable Neural Decoder for Topological Surface Codes by Meinerz, Park, Trebst (paper)