mlsanityData to 7 Oct 2026

Ab-initio study of interacting fermions at finite temperature with neural canonical transformation

arXiv 2105.08644 ↗First posted 18 May 2021cond-mat.str-el cond-mat.quant-gas cond-mat.stat-mechcited by 16

Not on the map. Why?

Abstract (arXiv)

We present a variational density matrix approach to the thermal properties of interacting fermions in the continuum. The variational density matrix is parametrized by a permutation equivariant many-body unitary transformation together with a discrete probabilistic model. The unitary transformation is implemented as a quantum counterpart of neural canonical transformation, which incorporates correlation effects via a flow of fermion coordinates. As the first application, we study electrons in a two-dimensional quantum dot with an interaction-induced crossover from Fermi liquid to Wigner molecule. The present approach provides accurate results in the low-temperature regime, where conventional quantum Monte Carlo methods face severe difficulties due to the fermion sign problem. The approach is general and flexible for further extensions, thus holds the promise to deliver new physical results on strongly correlated fermions in the context of ultracold quantum gases, condensed matter, and warm dense matter physics.

Key facts. Cited by 16 papers in the map (data through 7 Oct 2026). Builds on 28 of its 115 references that are in the map. First posted on arXiv 18 May 2021.

Builds on

28 of 115 references.

References · 28

  1. 2017Attention Is All You Needcited by 59,932
  2. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  3. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
  4. 2018Neural Ordinary Differential Equationscited by 3,733
  5. 2019Normalizing Flows for Probabilistic Modeling and Inferencecited by 1,315
  6. 2015Automatic differentiation in machine learning: a surveycited by 846
  7. 2019Augmented Neural ODEscited by 396
  8. 2019Sliced Score Matching: A Scalable Approach to Density and Score Estimationcited by 381
  9. 2018Invertible Residual Networkscited by 364
  10. 2019Monte Carlo Gradient Estimation in Machine Learningcited by 281
  11. 2019Ab-Initio Solution of the Many-Electron Schr"odinger Equation with Deep Neural Networkscited by 195
  12. 2019Flow-based generative models for Markov chain Monte Carlo in lattice field theorycited by 147
16 more
  1. 2019Deep neural network solution of the electronic Schr"odinger equationcited by 124
  2. 2020Equivariant flow-based sampling for lattice gauge theorycited by 102
  3. 2018Solving Statistical Mechanics Using Variational Autoregressive Networkscited by 99
  4. 2017Unsupervised Generative Modeling Using Matrix Product Statescited by 87
  5. 2020Targeted free energy estimation via learned mappingscited by 66
  6. 2019Equivariant Flows: sampling configurations for multi-body systems with symmetric energiescited by 57
  7. 2019Approximation Capabilities of Neural ODEs and Invertible Residual Networkscited by 56
  8. 2018Neural Network Renormalization Groupcited by 48
  9. 2020The Expressive Power of a Class of Normalizing Flow Modelscited by 35
  10. 2018Monge-Amp`ere Flow for Generative Modelingcited by 33
  11. 2020Better, Faster Fermionic Neural Networkscited by 28
  12. 2019Quantum Hamiltonian-Based Models and the Variational Quantum Thermalizer Algorithmcited by 27
  13. 2020Exchangeable Neural ODE for Set Modelingcited by 19
  14. 2019Neural Canonical Transformation with Symplectic Flowscited by 16
  15. 2020Phases of two-dimensional spinless lattice fermions with first-quantized deep neural-network quantum statescited by 15
  16. 2019Inferring the quantum density matrix with machine learningcited by 8

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 6; 2023: 3; 2024: 4; 2025: 1; 2026: 2.

16 papers.

Sort: