Ab-initio variational wave functions for the time-dependent many-electron Schr"odinger equation
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Abstract (arXiv)
Understanding the real-time evolution of many-electron quantum systems is essential for studying dynamical properties in condensed matter, quantum chemistry, and complex materials, yet it poses a significant theoretical and computational challenge. Our work introduces a variational approach for fermionic time-dependent wave functions, surpassing mean-field approximations by accurately capturing many-body correlations. Therefore, we employ time-dependent Jastrow factors and backflow transformations, which are enhanced through neural networks parameterizations. To compute the optimal time-dependent parameters, we utilize the time-dependent variational Monte Carlo technique and a new method based on Taylor-root expansions of the propagator, enhancing the accuracy of our simulations. The approach is demonstrated in three distinct systems. In all cases, we show clear signatures of many-body correlations in the dynamics. The results showcase the ability of our variational approach to accurately capture the time evolution, providing insight into the quantum dynamics of interacting electronic systems, beyond the capabilities of mean-field.
Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). Builds on 11 of its 132 references that are in the map. First posted on arXiv 12 Mar 2024.
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11 of 132 references.
References · 11
- 2019Ab-Initio Solution of the Many-Electron Schr"odinger Equation with Deep Neural Networkscited by 200
- 2019Deep neural network solution of the electronic Schr"odinger equationcited by 127
- 2022A Self-Attention Ansatz for Ab-initio Quantum Chemistrycited by 49
- 2022Ab-initio quantum chemistry with neural-network wavefunctionscited by 29
- 2021NetKet 3: Machine Learning Toolbox for Many-Body Quantum Systemscited by 28
- 2023Generalizing Neural Wave Functionscited by 28
- 2021Autoregressive neural-network wavefunctions for ab initio quantum chemistrycited by 21
- 2021Ab-initio study of interacting fermions at finite temperature with neural canonical transformationcited by 16
- 2023Neural Wave Functions for Superfluidscited by 13
- 2023Wasserstein Quantum Monte Carlo: A Novel Approach for Solving the Quantum Many-Body Schr"odinger Equationcited by 12
- 2023Pairing-based graph neural network for simulating quantum materialscited by 10
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Citing papers per year: 2024: 1; 2025: 4; 2026: 2.
7 papers.
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- 2026One More Time: Revisiting Neural Quantum States from a Reinforcement Learning Perspectivecited by 0
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- 2025Fermionic neural Gibbs states4 Dec 2025
- 2025Spin-Adapted Neural Network Wavefunctions in Real Space3 Nov 2025
- 2025Quantum computing and artificial intelligence: status and perspectives29 May 2025
- 2025Operator Learning for Schr"odinger Equation: Unitarity, Error Bounds, and Time Generalization23 May 2025
- 2024Are queries and keys always relevant? A case study on Transformer wave functions29 May 2024