SciPost Physics Codebases

SciPost Phys. Codebases 7 (2022) · published 24 August 2022

• doi: 10.21468/SciPostPhysCodeb.7
• pdf
• live repo (external)
• BiBTeX
• RIS
• Submissions/Reports
• 

Abstract

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 ansätze 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.

• Lin et al., Real-time dynamics of the Schwinger model as an open quantum system with Neural Density Operators
  J. High Energ. Phys. 2024, 211 (2024) [Crossref]
• Joshi et al., Quantum skyrmion dynamics studied by neural network quantum states
  Phys. Rev. B 110, 104411 (2024) [Crossref]
• Sinibaldi et al., Unbiasing time-dependent Variational Monte Carlo by projected quantum evolution
  Quantum 7, 1131 1131 (2023) [Crossref]
• Wu et al., Variational benchmarks for quantum many-body problems
  Science 386, 296 (2024) [Crossref]
• Liang et al., Deep learning representations for quantum many-body systems on heterogeneous hardware
  Mach. Learn.: Sci. Technol. 4, 015035 (2023) [Crossref]
• Ledinauskas et al., Scalable imaginary time evolution with neural network quantum states
  SciPost Phys. 15, 229 (2023) [Crossref]
• Mezera et al., Neural network quantum states analysis of the Shastry-Sutherland model
  SciPost Phys. Core 6, 088 (2023) [Crossref]
• Joshi et al., Ground state properties of quantum skyrmions described by neural network quantum states
  Phys. Rev. B 108, 094410 (2023) [Crossref]
• Rath et al., Framework for efficient ab initio electronic structure with Gaussian Process States
  Phys. Rev. B 107, 205119 (2023) [Crossref]
• Lange et al., From architectures to applications: a review of neural quantum states
  Quantum Sci. Technol. 9, 040501 (2024) [Crossref]
• Wu et al., Convergence Analysis of an Adaptively Regularized Natural Gradient Method
  IEEE Trans. Signal Process. 72, 2527 (2024) [Crossref]
• Vecsei et al., Lee-Yang theory of quantum phase transitions with neural network quantum states
  Phys. Rev. Research 5, 033116 (2023) [Crossref]
• Wei et al., Neural-shadow quantum state tomography
  Phys. Rev. Research 6, 023250 (2024) [Crossref]
• Passetti et al., Can Neural Quantum States Learn Volume-Law Ground States?
  Phys. Rev. Lett. 131, 036502 (2023) [Crossref]
• de Schoulepnikoff et al., Hybrid ground-state quantum algorithms based on neural Schrödinger forging
  Phys. Rev. Research 6, 023021 (2024) [Crossref]
• Szabó et al., Dynamics of the antiferromagnetic spin ice phase in pyrochlore spinels
  Phys. Rev. B 109, 104425 (2024) [Crossref]
• Vargas-Calderón et al., An empirical study of quantum dynamics as a ground state problem with neural quantum states
  Quantum Inf Process 22, 165 (2023) [Crossref]
• Drissi et al., Second-order optimization strategies for neural network quantum states
  Phil. Trans. R. Soc. A. 382, 20240057 (2024) [Crossref]
• Giuliani et al., Learning ground states of gapped quantum Hamiltonians with Kernel Methods
  Quantum 7, 1096 1096 (2023) [Crossref]
• Zhao et al., Empirical Sample Complexity of Neural Network Mixed State Reconstruction
  Quantum 8, 1358 1358 (2024) [Crossref]
• Bortone et al., Impact of conditional modelling for a universal autoregressive quantum state
  Quantum 8, 1245 1245 (2024) [Crossref]
• Keeble et al., Machine learning one-dimensional spinless trapped fermionic systems with neural-network quantum states
  Phys. Rev. A 108, 063320 (2023) [Crossref]
• Apte et al., Deep learning lattice gauge theories
  Phys. Rev. B 110, 165133 (2024) [Crossref]
• Nys et al., Real-time quantum dynamics of thermal states with neural thermofields
  Phys. Rev. B 109, 235120 (2024) [Crossref]
• Xie et al., $m^*$ of two-dimensional electron gas: A neural canonical transformation study
  SciPost Phys. 14, 154 (2023) [Crossref]
• Martyn et al., Variational Neural-Network Ansatz for Continuum Quantum Field Theory
  Phys. Rev. Lett. 131, 081601 (2023) [Crossref]
• Freitas et al., Synergy between deep neural networks and the variational Monte Carlo method for small 4HeN clusters
  Quantum 7, 1209 1209 (2023) [Crossref]
• Campaioli et al., Quantum Master Equations: Tips and Tricks for Quantum Optics, Quantum Computing, and Beyond
  PRX Quantum 5, 020202 (2024) [Crossref]
• Zhang et al., Ground state search by local and sequential updates of neural network quantum states
  Phys. Rev. B 107, 165149 (2023) [Crossref]
• Wei et al., Finding the Dynamics of an Integrable Quantum Many‐Body System via Machine Learning
  Advanced Physics Research 3, 2300078 (2024) [Crossref]
• Mellak et al., Deep neural networks as variational solutions for correlated open quantum systems
  Commun Phys 7, 268 (2024) [Crossref]
• Vicentini et al., Codebase release 3.4 for NetKet
  SciPost Phys. Codebases, 7-r3.4 (2022) [Crossref]
• Nys et al., Variational solutions to fermion-to-qubit mappings in two spatial dimensions
  Quantum 6, 833 833 (2022) [Crossref]
• Pescia et al., Message-passing neural quantum states for the homogeneous electron gas
  Phys. Rev. B 110, 035108 (2024) [Crossref]
• Bernheimer et al., Determinant- and derivative-free quantum Monte Carlo within the stochastic representation of wavefunctions
  Rep. Prog. Phys. 87, 118001 (2024) [Crossref]
• Donatella et al., Dynamics with autoregressive neural quantum states: Application to critical quench dynamics
  Phys. Rev. A 108, 022210 (2023) [Crossref]
• Roth et al., High-accuracy variational Monte Carlo for frustrated magnets with deep neural networks
  Phys. Rev. B 108, 054410 (2023) [Crossref]
• Sajjan et al., Imaginary components of out-of-time-order correlator and information scrambling for navigating the learning landscape of a quantum machine learning model
  Phys. Rev. Research 5, 013146 (2023) [Crossref]
• Crocioni et al., DeepRank2: Mining 3D Protein Structures with Geometric Deep Learning
  JOSS 9, 5983 (2024) [Crossref]
• Nys et al., Ab-initio variational wave functions for the time-dependent many-electron Schrödinger equation
  Nat Commun 15, 9404 (2024) [Crossref]
• Wu et al., A Real Neural Network State for Quantum Chemistry
  Mathematics 11, 1417 (2023) [Crossref]
• Atanasova et al., Stochastic representation of many-body quantum states
  Nat Commun 14, 3601 (2023) [Crossref]
• Wu et al., From tensor-network quantum states to tensorial recurrent neural networks
  Phys. Rev. Research 5, L032001 (2023) [Crossref]
• Szabó et al., Noncoplanar and chiral spin states on the way towards Néel ordering in fullerene Heisenberg models
  Phys. Rev. B 109, 054410 (2024) [Crossref]
• Chen et al., Empowering deep neural quantum states through efficient optimization
  Nat. Phys. 20, 1476 (2024) [Crossref]
• Rozalén Sarmiento et al., Machine learning the deuteron: new architectures and uncertainty quantification
  Eur. Phys. J. Plus 139, 189 (2024) [Crossref]
• Bayo et al., The percolating cluster is invisible to image recognition with deep learning
  New J. Phys. 25, 113041 (2023) [Crossref]
• Beck et al., Phase diagram of the J−Jd Heisenberg model on the maple leaf lattice: Neural networks and density matrix renormalization group
  Phys. Rev. B 109, 184422 (2024) [Crossref]
• Roca-Jerat et al., Transformer wave function for quantum long-range models
  Phys. Rev. B 110, 205147 (2024) [Crossref]
• Rende et al., A simple linear algebra identity to optimize large-scale neural network quantum states
  Commun Phys 7, 260 (2024) [Crossref]