mlsanityData to 14 Oct 2026

NetKet 3: Machine Learning Toolbox for Many-Body Quantum Systems

arXiv 2112.10526 ↗First posted 20 Dec 2021quant-ph cs.LG cs.MScited by 28

Not on the map. Why?

Abstract (arXiv)

We introduce version 3 of NetKet, the machine learning toolbox for many-body quantum physics. NetKet is built around neural-network quantum states and provides efficient algorithms for their evaluation and optimization. This new version is built on top of JAX, a differentiable programming and accelerated linear algebra framework for the Python programming language. The most significant new feature is the possibility to define arbitrary neural network ans"atze in pure Python code using the concise notation of machine-learning frameworks, which allows for just-in-time compilation as well as the implicit generation of gradients thanks to automatic differentiation. NetKet 3 also comes with support for GPU and TPU accelerators, advanced support for discrete symmetry groups, chunking to scale up to thousands of degrees of freedom, drivers for quantum dynamics applications, and improved modularity, allowing users to use only parts of the toolbox as a foundation for their own code.

Key facts. Cited by 28 papers in the map (data through 14 Oct 2026). Builds on 11 of its 99 references that are in the map. First posted on arXiv 20 Dec 2021.

Builds on

11 of 99 references.

References · 11

  1. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,305
  2. 2017Deep Setscited by 1,421
  3. 2016Group Equivariant Convolutional Networkscited by 1,165
  4. 2018This Looks Like That: Deep Learning for Interpretable Image Recognitioncited by 737
  5. 2019Quantum Natural Gradientcited by 106
  6. 2018Solving Statistical Mechanics Using Variational Autoregressive Networkscited by 100
  7. 2019Deep autoregressive models for the efficient variational simulation of many-body quantum systemscited by 64
  8. 2017Neural-Network Quantum States, String-Bond States, and Chiral Topological Statescited by 31
  9. 2020Neural network wave functions and the sign problemcited by 20
  10. 2019Geometry of learning neural quantum statescited by 14
  11. 2019Summarizing CPU and GPU Design Trends with Product Datacited by 10

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 3; 2023: 8; 2024: 4; 2025: 7; 2026: 6.

28 papers.

Sort:

By area:

  1. 2023Wasserstein Quantum Monte Carlo: A Novel Approach for Solving the Quantum Many-Body Schr"odinger Equationcited by 12
  2. 2023ANTN: Bridging Autoregressive Neural Networks and Tensor Networks for Quantum Many-Body Simulationcited by 8
  3. 2022m^ast of two-dimensional electron gas: a neural canonical transformation studycited by 7
  4. 2024Ab-initio variational wave functions for the time-dependent many-electron Schr"odinger equationcited by 7
  5. 2023Forward Laplacian: A New Computational Framework for Neural Network-based Variational Monte Carlocited by 6
  6. 2024Are queries and keys always relevant? A case study on Transformer wave functionscited by 6
  7. 2024When can classical neural networks represent quantum states?cited by 6
  8. 2025Interpretable representation learning of quantum data enabled by probabilistic variational autoencoderscited by 6
  9. 2024Approximately-symmetric neural networks for quantum spin liquidscited by 5
  10. 2023Q-Flow: Generative Modeling for Differential Equations of Open Quantum Dynamics with Normalizing Flowscited by 4
  11. 2023Empirical Sample Complexity of Neural Network Mixed State Reconstructioncited by 4
  12. 2023Deep Stochastic Mechanicscited by 3
16 more
  1. 2025Functional Neural Wavefunction Optimizationcited by 3
  2. 2026Discovering quantum phenomena with Interpretable Machine Learningcited by 3
  3. 2023Learning ground states of gapped quantum Hamiltonians with Kernel Methodscited by 2
  4. 2025Probabilistic Computers for Neural Quantum Statescited by 1
  5. 2026Stochastic Reconfiguration as Statistical Filtering for Overparameterized Neural Quantum Statescited by 1
  6. 2022An Empirical Study of Quantum Dynamics as a Ground State Problem with Neural Quantum Statescited by 0
  7. 2022Neural network quantum state with proximal optimization: a ground-state searching scheme based on variational Monte Carlocited by 0
  8. 2023Hybrid Ground-State Quantum Algorithms based on Neural Schr"odinger Forgingcited by 0
  9. 2025Variational decision diagrams for quantum-inspired machine learning applicationscited by 0
  10. 2025DeepQuark: A Deep-Neural-Network Approach to Multiquark Bound Statescited by 0
  11. 2025Fermionic neural Gibbs statescited by 0
  12. 2025Exploring the Effect of Basis Rotation on NQS Performancecited by 0
  13. 2026Tensor-Augmented Convolutional Neural Networks: Enhancing Expressivity with Generic Tensor Kernelscited by 0
  14. 2026Hyperbolic Restricted Boltzmann Machine Neural Quantum Statecited by 0
  15. 2026Apparent Compression, Real Stability: The Intrinsic Dimension of Learning a Quantum Wavefunctioncited by 0
  16. 2026Probabilistic Algorithms for Ising Machines from Optimization to Generative AIcited by 0