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Ab-initio variational wave functions for the time-dependent many-electron Schrödinger equation (2403.07447v3)

Published 12 Mar 2024 in cond-mat.str-el, cs.LG, physics.chem-ph, physics.comp-ph, and quant-ph

Abstract: 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.

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References (59)
  1. K. Choo, A. Mezzacapo, and G. Carleo, Fermionic neural-network states for ab-initio electronic structure, Nature communications 11, 2368 (2020).
  2. L. Savary and L. Balents, Quantum spin liquids: a review, Reports on Progress in Physics 80, 016502 (2016).
  3. A. Filinov, M. Bonitz, and Y. E. Lozovik, Wigner crystallization in mesoscopic 2d electron systems, Physical review letters 86, 3851 (2001).
  4. A. Filinov, Y. E. Lozovik, and M. Bonitz, Path integral simulations of crystallization of quantum confined electrons, physica status solidi (b) 221, 231 (2000).
  5. S. M. Reimann and M. Manninen, Electronic structure of quantum dots, Reviews of modern physics 74, 1283 (2002).
  6. S. Giorgini, L. P. Pitaevskii, and S. Stringari, Theory of ultracold atomic fermi gases, Reviews of Modern Physics 80, 1215 (2008).
  7. P. A. M. Dirac, Note on Exchange Phenomena in the Thomas Atom, Mathematical Proceedings of the Cambridge Philosophical Society 26, 376 (1930).
  8. J. Frenkel et al., Wave mechanics, advanced general theory, Vol. 436 (Oxford, 1934).
  9. A. McLachlan and M. Ball, Time-dependent hartree—fock theory for molecules, Reviews of Modern Physics 36, 844 (1964).
  10. H.-D. Meyer, U. Manthe, and L. S. Cederbaum, The multi-configurational time-dependent hartree approach, Chemical Physics Letters 165, 73 (1990).
  11. D. A. Micha and K. Runge, Time-dependent many-electron approach to slow ion-atom collisions: The coupling of electronic and nuclear motions, Physical Review A 50, 322 (1994).
  12. K. Yabana and G. Bertsch, Time-dependent local-density approximation in real time, Physical Review B 54, 4484 (1996).
  13. C. M. Isborn, X. Li, and J. C. Tully, Time-dependent density functional theory ehrenfest dynamics: Collisions between atomic oxygen and graphite clusters, The Journal of chemical physics 126 (2007).
  14. T. Sato and K. L. Ishikawa, Time-dependent complete-active-space self-consistent-field method for multielectron dynamics in intense laser fields, Physical Review A 88, 023402 (2013).
  15. H. Miyagi and L. B. Madsen, Time-dependent restricted-active-space self-consistent-field theory for laser-driven many-electron dynamics, Physical Review A 87, 062511 (2013).
  16. H. Miyagi and L. B. Madsen, Time-dependent restricted-active-space self-consistent-field theory for laser-driven many-electron dynamics. ii. extended formulation and numerical analysis, Physical Review A 89, 063416 (2014).
  17. T. Sato and K. L. Ishikawa, Time-dependent multiconfiguration self-consistent-field method based on the occupation-restricted multiple-active-space model for multielectron dynamics in intense laser fields, Physical Review A 91, 023417 (2015).
  18. P. Krause, T. Klamroth, and P. Saalfrank, Time-dependent configuration-interaction calculations of laser-pulse-driven many-electron dynamics: Controlled dipole switching in lithium cyanide, The Journal of chemical physics 123 (2005).
  19. E. Luppi and M. Head-Gordon, Computation of high-harmonic generation spectra of h2 and n2 in intense laser pulses using quantum chemistry methods and time-dependent density functional theory, Molecular Physics 110, 909 (2012).
  20. P. J. Lestrange, M. R. Hoffmann, and X. Li, Time-dependent configuration interaction using the graphical unitary group approach: Nonlinear electric properties, in Advances in Quantum Chemistry, Vol. 76 (Elsevier, 2018) pp. 295–313.
  21. I. S. Ulusoy, Z. Stewart, and A. K. Wilson, The role of the ci expansion length in time-dependent studies, The Journal of chemical physics 148 (2018).
  22. L. S. Cederbaum and J. Zobeley, Ultrafast charge migration by electron correlation, Chemical Physics Letters 307, 205 (1999).
  23. R. Santra and L. S. Cederbaum, Complex absorbing potentials in the framework of electron propagator theory. i. general formalism, The Journal of chemical physics 117, 5511 (2002).
  24. A. I. Kuleff and L. S. Cederbaum, Tracing ultrafast interatomic electronic decay processes in real time and space, Physical review letters 98, 083201 (2007).
  25. A. I. Kuleff and L. S. Cederbaum, Ultrafast correlation-driven electron dynamics, Journal of Physics B: Atomic, Molecular and Optical Physics 47, 124002 (2014).
  26. S. P. Neville and M. S. Schuurman, A general approach for the calculation and characterization of x-ray absorption spectra, The Journal of chemical physics 149 (2018).
  27. D. R. Nascimento and A. E. DePrince III, Linear absorption spectra from explicitly time-dependent equation-of-motion coupled-cluster theory, Journal of chemical theory and computation 12, 5834 (2016).
  28. C. Huber and T. Klamroth, Explicitly time-dependent coupled cluster singles doubles calculations of laser-driven many-electron dynamics, The Journal of chemical physics 134 (2011).
  29. D. R. Nascimento and A. E. DePrince III, Simulation of near-edge x-ray absorption fine structure with time-dependent equation-of-motion coupled-cluster theory, The journal of physical chemistry letters 8, 2951 (2017).
  30. D. R. Nascimento and A. E. DePrince, A general time-domain formulation of equation-of-motion coupled-cluster theory for linear spectroscopy, The Journal of Chemical Physics 151 (2019).
  31. A. S. Skeidsvoll, A. Balbi, and H. Koch, Time-dependent coupled-cluster theory for ultrafast transient-absorption spectroscopy, Physical Review A 102, 023115 (2020).
  32. M. Eckstein, M. Kollar, and P. Werner, Interaction quench in the hubbard model: Relaxation of the spectral function and the optical conductivity, Physical Review B 81, 115131 (2010).
  33. M. Schiró and M. Fabrizio, Time-dependent mean field theory for quench dynamics in correlated electron systems, Physical review letters 105, 076401 (2010).
  34. G. Vidal, Efficient simulation of one-dimensional quantum many-body systems, Physical review letters 93, 040502 (2004).
  35. J. Haegeman, T. J. Osborne, and F. Verstraete, Post-matrix product state methods: To tangent space and beyond, Physical Review B 88, 075133 (2013).
  36. M. Cazalilla and J. Marston, Time-dependent density-matrix renormalization group: A systematic method for the study of quantum many-body out-of-equilibrium systems, Physical review letters 88, 256403 (2002).
  37. G. Carleo and M. Troyer, Solving the quantum many-body problem with artificial neural networks, Science 355, 602 (2017).
  38. J. Hermann, Z. Schätzle, and F. Noé, Deep-neural-network solution of the electronic schrödinger equation, Nature Chemistry 12, 891 (2020).
  39. M. Schmitt and M. Heyl, Quantum many-body dynamics in two dimensions with artificial neural networks, Physical Review Letters 125, 100503 (2020).
  40. I. L. Gutiérrez and C. B. Mendl, Real time evolution with neural-network quantum states, Quantum 6, 627 (2022).
  41. M. Gartner, F. Mazzanti, and R. Zillich, Time-dependent variational monte carlo study of the dynamic response of bosons in an optical lattice, SciPost Physics 13, 025 (2022).
  42. K. Ido, T. Ohgoe, and M. Imada, Time-dependent many-variable variational monte carlo method for nonequilibrium strongly correlated electron systems, Physical Review B 92, 245106 (2015).
  43. A. Szabo and N. S. Ostlund, Modern Quantum Chemistry: Introduction to Advanced Electronic Structure Theory, new edition ed. (Dover Publications, Mineola, New York, 1996).
  44. V. Gritsev, P. Barmettler, and E. Demler, Scaling approach to quantum non-equilibrium dynamics of many-body systems, New journal of Physics 12, 113005 (2010).
  45. D. Luo, D. D. Dai, and L. Fu, Pairing-based graph neural network for simulating quantum materials, arXiv preprint arXiv:2311.02143  (2023).
  46. A. Gnech, B. Fore, and A. Lovato, Distilling the essential elements of nuclear binding via neural-network quantum states, arXiv preprint arXiv:2308.16266  (2023).
  47. B. T. Diroll, Colloidal quantum wells for optoelectronic devices, Journal of Materials Chemistry C 8, 10628 (2020).
  48. M. Richter, Nanoplatelets as material system between strong confinement and weak confinement, Physical Review Materials 1, 016001 (2017).
  49. J. Planelles and J. I. Climente, A simple variational quantum monte carlo-effective mass approach for excitons and trions in quantum dots, Computer Physics Communications 261, 107782 (2021).
  50. L. Jacak, P. Hawrylak, and A. Wojs, Quantum dots (Springer Science & Business Media, 2013).
  51. D. Bimberg, M. Grundmann, and N. N. Ledentsov, Quantum dot heterostructures (John Wiley & Sons, 1999).
  52. T. Chakraborty, Quantum Dots: A survey of the properties of artificial atoms (Elsevier, 1999).
  53. L. Serra, A. Puente, and E. Lipparini, Breathing modes of 2-d quantum dots with elliptical shape in magnetic fields, International journal of quantum chemistry 91, 483 (2003).
  54. H. Xie, L. Zhang, and L. Wang, Ab-initio study of interacting fermions at finite temperature with neural canonical transformation, arXiv preprint arXiv:2105.08644  (2021).
  55. D. Häfner and F. Vicentini, mpi4jax: Zero-copy mpi communication of jax arrays, Journal of Open Source Software 6, 3419 (2021).
  56. P. Jordan and E. P. Wigner, Über das paulische äquivalenzverbot (Springer, 1993).
  57. J. Nys and G. Carleo, Variational solutions to fermion-to-qubit mappings in two spatial dimensions, Quantum 6, 833 (2022).
  58. T. D. Barrett, A. Malyshev, and A. Lvovsky, Autoregressive neural-network wavefunctions for ab initio quantum chemistry, Nature Machine Intelligence 4, 351 (2022).
  59. E. Anisimovas and A. Matulis, Energy spectra of few-electron quantum dots, Journal of Physics: Condensed Matter 10, 601 (1998).
Citations (6)

Summary

  • The paper introduces a variational approach that overcomes mean-field limitations by accurately capturing many-body electron correlations.
  • It employs time-dependent variational Monte Carlo with Jastrow factors, backflow transformations, and neural networks to solve the TDSE.
  • Results across harmonic models, molecular systems, and quantum dots demonstrate enhanced accuracy in simulating complex quantum dynamics.

A Novel Variational Approach for Simulating Time-dependent Many-electron Systems

Introduction

The paper of many-electron systems is pivotal for understanding a myriad of phenomena in quantum chemistry and condensed matter physics. Traditional methods, such as time-dependent Hartree-Fock (TDHF), though widely used, often fall short in accurately capturing the complexities of electron-electron interactions due to their mean-field nature. In this context, the paper by Jannes Nys, Gabriel Pescia, and Giuseppe Carleo presents a significant advancement by introducing a variational methodology to simulate the time evolution of many-electron systems, going beyond mean-field approximations. Their approach, which employs time-dependent variational Monte Carlo (tVMC) with a focus on incorporating many-body correlations using Jastrow factors and backflow transformations, opens new avenues for studying quantum dynamics in an array of systems.

Methodology

The authors detail a comprehensive methodological framework to approximate the time-evolving state of a quantum system governed by the time-dependent Schrödinger equation (TDSE). Their key innovation lies in parameterizing the wave function's evolution, facilitated by the implementation of time-dependent variational principles (TDVP), specifically MacLachlan's variational principle. Through tVMC, this approach enables efficient computation of optimal time-dependent parameters, overcoming the previous limitations encountered in describing real-time quantum dynamics.

Wave-function Models

The paper elaborates on the construction of variational wave-function models adapted for fermionic systems, featuring time-dependent Jastrow factors and backflow transformations to account for electron correlations. Furthermore, the integration of neural networks provides a novel avenue for parameterizing these functions, showcasing the fusion of traditional variational techniques with contemporary machine learning methodologies.

Results

The efficacy of the proposed method is demonstrated across three distinct systems, namely:

  1. The Harmonic Interaction Model: Showcasing clear signatures of many-body correlations in dynamics not captured by mean-field methods.
  2. Dynamics of a Diatomic Molecule in Intense Laser Fields: Illustrating the method's capability to accurately capture time-evolution in complex molecular systems.
  3. Quenched Quantum Dot: Highlighting the importance of including many-body correlations for understanding strongly interacting electronic systems.

The results underline the significant advantage of the variational approach in accurately capturing the quantum dynamics beyond the capabilities offered by mean-field approximations.

Conclusions and Future Work

This paper establishes a groundbreaking variational framework that effectively captures the time evolution of many-electron systems, marking a significant step forward in the simulation of quantum dynamics. The inclusion of many-body correlations opens up new possibilities for detailed investigations into the electronic structure of materials and molecules. Looking ahead, this method sets the stage for future explorations into larger systems and more complex scenarios, potentially revolutionizing our understanding of quantum materials and reactions.

The presented work emphasizes the ongoing evolution of computational methods in quantum physics, highlighting the convergence of traditional physics-based models and modern computational techniques. It paves the way for more sophisticated and accurate simulations, which are crucial for advancing our understanding of quantum systems.