Ab-initio study of interacting fermions at finite temperature with neural canonical transformation
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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.
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28 of 115 references.
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Citing papers per year: 2022: 6; 2023: 3; 2024: 4; 2025: 1; 2026: 2.
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- 2022Ab-initio quantum chemistry with neural-network wavefunctionscited by 28
- 2022Simulating 2+1D Lattice Quantum Electrodynamics at Finite Density with Neural Flow Wavefunctionscited by 10
- 2023Pairing-based graph neural network for simulating quantum materialscited by 9
- 2022m^ast of two-dimensional electron gas: a neural canonical transformation studycited by 7
- 2024Ab-initio variational wave functions for the time-dependent many-electron Schr"odinger equationcited by 7
- 2022Deep Variational Free Energy Approach to Dense Hydrogencited by 6
- 2024Deep generative modelling of canonical ensemble with differentiable thermal propertiescited by 2
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- 2022Waveflow: boundary-conditioned normalizing flows applied to fermionic wavefunctionscited by 1
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- 2026Convergence theory for Hermite approximations under adaptive coordinate transformations18 Apr 2026
- 2025Accurate and thermodynamically consistent hydrogen equation of state for planetary modeling with flow matching17 Jan 2025
- 2024Inducing Riesz and orthonormal bases in L2 via composition operators25 Jun 2024
- 2024A Theoretical Framework for an Efficient Normalizing Flow-Based Solution to the Electronic Schrodinger Equation28 May 2024
- 2024Deep generative modelling of canonical ensemble with differentiable thermal properties29 Apr 2024
- 2024Ab-initio variational wave functions for the time-dependent many-electron Schr"odinger equation12 Mar 2024
- 2023Sorting Out Quantum Monte Carlo9 Nov 2023
- 2023Pairing-based graph neural network for simulating quantum materials3 Nov 2023
- 2023Artificial intelligence for artificial materials: moir'e atom14 Mar 2023
- 2022Simulating 2+1D Lattice Quantum Electrodynamics at Finite Density with Neural Flow Wavefunctions14 Dec 2022
- 2022Waveflow: boundary-conditioned normalizing flows applied to fermionic wavefunctions27 Nov 2022
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- 2022Deep Variational Free Energy Approach to Dense Hydrogen13 Sep 2022
- 2022Ab-initio quantum chemistry with neural-network wavefunctions26 Aug 2022
- 2022Numerical and geometrical aspects of flow-based variational quantum Monte Carlo28 Mar 2022
- 2022m^ast of two-dimensional electron gas: a neural canonical transformation study10 Jan 2022
- 2022Ab-initio quantum chemistry with neural-network wavefunctionscited by 28
- 2022Simulating 2+1D Lattice Quantum Electrodynamics at Finite Density with Neural Flow Wavefunctionscited by 10
- 2023Pairing-based graph neural network for simulating quantum materialscited by 9
- 2022m^ast of two-dimensional electron gas: a neural canonical transformation studycited by 7
- 2024Ab-initio variational wave functions for the time-dependent many-electron Schr"odinger equationcited by 7
- 2022Deep Variational Free Energy Approach to Dense Hydrogencited by 6
- 2024Deep generative modelling of canonical ensemble with differentiable thermal propertiescited by 2
- 2022Numerical and geometrical aspects of flow-based variational quantum Monte Carlocited by 1
- 2022Waveflow: boundary-conditioned normalizing flows applied to fermionic wavefunctionscited by 1
- 2023Artificial intelligence for artificial materials: moir'e atomcited by 1
- 2023Sorting Out Quantum Monte Carlocited by 1
- 2024A Theoretical Framework for an Efficient Normalizing Flow-Based Solution to the Electronic Schrodinger Equationcited by 0
12 most cited of 16 All 16 (JSON)
- 2026Quantum Dynamics via Score Matching on Bohmian Trajectories28 Apr 2026
- 2026Convergence theory for Hermite approximations under adaptive coordinate transformations18 Apr 2026
- 2025Accurate and thermodynamically consistent hydrogen equation of state for planetary modeling with flow matching17 Jan 2025
- 2024Inducing Riesz and orthonormal bases in L2 via composition operators25 Jun 2024
- 2024A Theoretical Framework for an Efficient Normalizing Flow-Based Solution to the Electronic Schrodinger Equation28 May 2024
- 2024Deep generative modelling of canonical ensemble with differentiable thermal properties29 Apr 2024
- 2024Ab-initio variational wave functions for the time-dependent many-electron Schr"odinger equation12 Mar 2024
- 2023Sorting Out Quantum Monte Carlo9 Nov 2023
- 2023Pairing-based graph neural network for simulating quantum materials3 Nov 2023
- 2023Artificial intelligence for artificial materials: moir'e atom14 Mar 2023
- 2022Simulating 2+1D Lattice Quantum Electrodynamics at Finite Density with Neural Flow Wavefunctions14 Dec 2022
- 2022Waveflow: boundary-conditioned normalizing flows applied to fermionic wavefunctions27 Nov 2022
12 newest of 16 All 16 (JSON)