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Multiorbital dynamical mean-field theory with a complex-time solver

Yang Yu1, Lei Zhang1, Emanuel Gull1,2, Xiaodong Cao3,4,*, and Xinyang Dong5,†

  • *Contact author: xdcao@ustc.edu.cn
  • †Contact author: dongxy@iphy.ac.cn

Phys. Rev. Research 8, 023142 – Published 11 May, 2026

DOI: https://doi.org/10.1103/yqv4-4vjx

Abstract

We present the combination of a complex-time tensor-network impurity solver with an analytic continuation scheme based on exponential fitting as an efficient framework for single and multiorbital dynamical mean-field calculations. By performing time evolution along a complex-time contour, the approach balances computational cost with the difficulty of spectral recovery, offering greater flexibility than methods confined to the real or imaginary axis. By complementing the complex-time evolution with an exponential fitting scheme, we faithfully extract real-time information at negligible cost. The resulting method obtains high-resolution spectra at a significantly lower computational cost than real-time evolution, offering a promising tool for ab initio studies of strongly correlated materials.

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References (82)

  1. W. Metzner and D. Vollhardt, Correlated lattice fermions in d=∞ dimensions, Phys. Rev. Lett. 62, 324 (1989).
  2. A. Georges, G. Kotliar, W. Krauth, and M. J. Rozenberg, Dynamical mean-field theory of strongly correlated fermion systems and the limit of infinite dimensions, Rev. Mod. Phys. 68, 13 (1996).
  3. G. Kotliar, S. Y. Savrasov, K. Haule, V. S. Oudovenko, O. Parcollet, and C. A. Marianetti, Electronic structure calculations with dynamical mean-field theory, Rev. Mod. Phys. 78, 865 (2006).
  4. T. Pruschke, Perturbation theory of the Anderson model at finite U, Physica B 163, 553 (1990).
  5. H. Keiter and J. C. Kimball, Perturbation technique for the Anderson Hamiltonian, Phys. Rev. Lett. 25, 672 (1970).
  6. T. Pruschke and N. Grewe, The Anderson model with finite Coulomb repulsion, Z. Phys. B 74, 439 (1989).
  7. A. N. Rubtsov, V. V. Savkin, and A. I. Lichtenstein, Continuous-time quantum Monte Carlo method for fermions, Phys. Rev. B 72, 035122 (2005).
  8. P. Werner, A. Comanac, L. de’ Medici, M. Troyer, and A. J. Millis, Continuous-time solver for quantum impurity models, Phys. Rev. Lett. 97, 076405 (2006).
  9. E. Gull, P. Werner, O. Parcollet, and M. Troyer, Continuous-time auxiliary-field Monte Carlo for quantum impurity models, Europhys. Lett. 82, 57003 (2008).
  10. E. Gull, A. J. Millis, A. I. Lichtenstein, A. N. Rubtsov, M. Troyer, and P. Werner, Continuous-time Monte Carlo methods for quantum impurity models, Rev. Mod. Phys. 83, 349 (2011).
  11. G. Cohen, E. Gull, D. R. Reichman, A. J. Millis, and E. Rabani, Numerically exact long-time magnetization dynamics at the nonequilibrium Kondo crossover of the Anderson impurity model, Phys. Rev. B 87, 195108 (2013).
  12. M. Caffarel and W. Krauth, Exact diagonalization approach to correlated fermions in infinite dimensions: Mott transition and superconductivity, Phys. Rev. Lett. 72, 1545 (1994).
  13. M. Capone, L. de’ Medici, and A. Georges, Solving the dynamical mean-field theory at very low temperatures using the Lanczos exact diagonalization, Phys. Rev. B 76, 245116 (2007).
  14. S. Iskakov and M. Danilov, Exact diagonalization library for quantum electron models, Comput. Phys. Commun. 225, 128 (2018).
  15. D. Zgid, E. Gull, and G. K.-L. Chan, Truncated configuration interaction expansions as solvers for correlated quantum impurity models and dynamical mean-field theory, Phys. Rev. B 86, 165128 (2012).
  16. A. Shee and D. Zgid, Coupled cluster as an impurity solver for Green’s function embedding methods, J. Chem. Theory Comput. 15, 6010 (2019).
  17. T. Zhu, C. A. Jiménez-Hoyos, J. McClain, T. C. Berkelbach, and G. K.-L. Chan, Coupled-cluster impurity solvers for dynamical mean-field theory, Phys. Rev. B 100, 115154 (2019).
  18. K. G. Wilson, The renormalization group: Critical phenomena and the Kondo problem, Rev. Mod. Phys. 47, 773 (1975).
  19. T. Pruschke, R. Bulla, and M. Jarrell, Low-energy scale of the periodic Anderson model, Phys. Rev. B 61, 12799 (2000).
  20. R. Bulla, T. A. Costi, and T. Pruschke, Numerical renormalization group method for quantum impurity systems, Rev. Mod. Phys. 80, 395 (2008).
  21. S. R. White, Density matrix formulation for quantum renormalization groups, Phys. Rev. Lett. 69, 2863 (1992).
  22. S. R. White, Density-matrix algorithms for quantum renormalization groups, Phys. Rev. B 48, 10345 (1993).
  23. U. Schollwöck, The density-matrix renormalization group in the age of matrix product states, Ann. Phys. 326, 96 (2011).
  24. R. Orús, Advances on tensor network theory: Symmetries, fermions, entanglement, and holography, Eur. Phys. J. B 87, 280 (2014).
  25. F. A. Wolf, A. Go, I. P. McCulloch, A. J. Millis, and U. Schollwöck, Imaginary-time matrix product state impurity solver for dynamical mean-field theory, Phys. Rev. X 5, 041032 (2015).
  26. Y. Núñez Fernández, M. Jeannin, P. T. Dumitrescu, T. Kloss, J. Kaye, O. Parcollet, and X. Waintal, Learning Feynman diagrams with tensor trains, Phys. Rev. X 12, 041018 (2022).
  27. A. J. Kim and P. Werner, Strong coupling impurity solver based on quantics tensor cross interpolation, Phys. Rev. B 111, 125120 (2025).
  28. S. Matsuura, H. Shinaoka, P. Werner, and N. Tsuji, Tensor cross interpolation approach for quantum impurity problems based on the weak-coupling expansion, Phys. Rev. B 111, 155150 (2025).
  29. L. Geng, A. J. Kim, and P. Werner, Third-order strong-coupling impurity solver for real-frequency dynamical mean field theory: Accurate spectral functions for antiferromagnetic and photodoped states, Phys. Rev. B 112, 245119 (2025).
  30. M. Jarrell and J. Gubernatis, Bayesian inference and the analytic continuation of imaginary-time quantum Monte Carlo data, Phys. Rep. 269, 133 (1996).
  31. A. W. Sandvik, Stochastic method for analytic continuation of quantum Monte Carlo data, Phys. Rev. B 57, 10287 (1998).
  32. H. J. Vidberg and J. W. Serene, Solving the Eliashberg equations by means of n-point Padé approximants, J. Low Temp. Phys. 29, 179 (1977).
  33. J. Fei, C.-N. Yeh, and E. Gull, Nevanlinna analytical continuation, Phys. Rev. Lett. 126, 056402 (2021).
  34. Z. Huang, E. Gull, and L. Lin, Robust analytic continuation of Green’s functions via projection, pole estimation, and semidefinite relaxation, Phys. Rev. B 107, 075151 (2023).
  35. L. Ying, Pole recovery from noisy data on imaginary axis, J. Sci. Comput. 92, 107 (2022).
  36. L. Ying, Analytic continuation from limited noisy Matsubara data, J. Comput. Phys. 469, 111549 (2022).
  37. L. Zhang and E. Gull, Minimal pole representation and controlled analytic continuation of Matsubara response functions, Phys. Rev. B 110, 035154 (2024).
  38. K. A. Hallberg, Density-matrix algorithm for the calculation of dynamical properties of low-dimensional systems, Phys. Rev. B 52, R9827 (1995).
  39. P. E. Dargel, A. Honecker, R. Peters, R. M. Noack, and T. Pruschke, Adaptive Lanczos-vector method for dynamic properties within the density matrix renormalization group, Phys. Rev. B 83, 161104(R) (2011).
  40. P. E. Dargel, A. Wöllert, A. Honecker, I. P. McCulloch, U. Schollwöck, and T. Pruschke, Lanczos algorithm with matrix product states for dynamical correlation functions, Phys. Rev. B 85, 205119 (2012).
  41. O. Kovalska, J. von Delft, and A. Gleis, Tangent space Krylov computation of real-frequency spectral functions: Influence of density-assisted hopping on 2D Mott physics, arXiv:2510.07279.
  42. S. R. White and A. E. Feiguin, Real-time evolution using the density matrix renormalization group, Phys. Rev. Lett. 93, 076401 (2004).
  43. J. Haegeman, J. I. Cirac, T. J. Osborne, I. Pižorn, H. Verschelde, and F. Verstraete, Time-dependent variational principle for quantum lattices, Phys. Rev. Lett. 107, 070601 (2011).
  44. J. Haegeman, T. J. Osborne, and F. Verstraete, Post-matrix product state methods: To tangent space and beyond, Phys. Rev. B 88, 075133 (2013).
  45. J. Haegeman, C. Lubich, I. Oseledets, B. Vandereycken, and F. Verstraete, Unifying time evolution and optimization with matrix product states, Phys. Rev. B 94, 165116 (2016).
  46. L. Vanderstraeten, J. Haegeman, and F. Verstraete, Tangent-space methods for uniform matrix product states, SciPost Phys. Lect. Notes, 7 (2019).
  47. M. Ganahl, M. Aichhorn, H. G. Evertz, P. Thunström, K. Held, and F. Verstraete, Efficient DMFT impurity solver using real-time dynamics with matrix product states, Phys. Rev. B 92, 155132 (2015).
  48. D. Bauernfeind, M. Zingl, R. Triebl, M. Aichhorn, and H. G. Evertz, Fork tensor-product states: Efficient multiorbital real-time DMFT solver, Phys. Rev. X 7, 031013 (2017).
  49. X. Cao, Y. Lu, P. Hansmann, and M. W. Haverkort, Tree tensor-network real-time multiorbital impurity solver: Spin-orbit coupling and correlation functions in Sr2RuO4, Phys. Rev. B 104, 115119 (2021).
  50. X. Cao, Y. Lu, E. M. Stoudenmire, and O. Parcollet, Dynamical correlation functions from complex time evolution, Phys. Rev. B 109, 235110 (2024).
  51. M. Grundner, P. Westhoff, F. B. Kugler, O. Parcollet, and U. Schollwöck, Complex time evolution in tensor networks and time-dependent Green’s functions, Phys. Rev. B 109, 155124 (2024).
  52. K. Guther, W. Dobrautz, O. Gunnarsson, and A. Alavi, Time propagation and spectroscopy of fermionic systems using a stochastic technique, Phys. Rev. Lett. 121, 056401 (2018).
  53. Z. Wang, P. McClarty, D. Dankova, A. Honecker, and A. Wietek, Spectroscopy and complex-time correlations using minimally entangled typical thermal states, Phys. Rev. B 113, 024406 (2026).
  54. S. Paeckel, Spectral decomposition and high-accuracy Greens functions: Overcoming the Nyquist-Shannon limit via complex-time Krylov expansion, Phys. Rev. Lett. 136, 160401 (2026).
  55. L. Zhang, A. Erpenbeck, Y. Yu, and E. Gull, Minimal pole representation for spectral functions, J. Chem. Phys. 162, 214111 (2025).
  56. A. Erpenbeck, Y. Zhu, Y. Yu, L. Zhang, R. Gerum, O. Goulko, C. Yang, G. Cohen, and E. Gull, Compact representation and long-time extrapolation of real-time data for quantum systems, Phys. Rev. B 113, 115129 (2026).
  57. A. F. Kemper, C. Yang, and E. Gull, Denoising and extension of response functions in the time domain, Phys. Rev. Lett. 132, 160403 (2024).
  58. G. R. de Prony, Essai experimental et analytique: Sur les lois de la dilatabilite des fluides elastique et sur celles de la force expansive de la vapeur de l'eau et de la vapeur de l'alkool, a differentes temperatures, J. Polytech. ou Bull. du Trav. fait Ec. Cent. Trav. Publics 1, 24 (1795).
  59. R. Roy and T. Kailath, Esprit-estimation of signal parameters via rotational invariance techniques, IEEE Trans. Acoust. Speech Signal Process. 37, 984 (1989).
  60. Y. Hua and T. K. Sarkar, Matrix pencil method for estimating parameters of exponentially damped/undamped sinusoids in noise, IEEE Trans. Acoust. Speech Signal Process. 38, 814 (1990).
  61. T. Sarkar and O. Pereira, Using the matrix pencil method to estimate the parameters of a sum of complex exponentials, IEEE Antennas Propag. Mag. 37, 48 (1995).
  62. R. Schmidt, Multiple emitter location and signal parameter estimation, IEEE Trans. Antennas Propag. 34, 276 (1986).
  63. P. J. SCHMID, Dynamic mode decomposition of numerical and experimental data, J. Fluid Mech. 656, 5 (2010).
  64. D. Potts and M. Tasche, Parameter estimation for nonincreasing exponential sums by Prony-like methods, Linear Algebra Appl. 439, 1024 (2013).
  65. L. Zhang, Y. Yu, and E. Gull, Minimal pole representation and analytic continuation of matrix-valued correlation functions, Phys. Rev. B 110, 235131 (2024).
  66. Y.-Y. Shi, L.-M. Duan, and G. Vidal, Classical simulation of quantum many-body systems with a tree tensor network, Phys. Rev. A 74, 022320 (2006).
  67. V. Murg, F. Verstraete, O. Legeza, and R. M. Noack, Simulating strongly correlated quantum systems with tree tensor networks, Phys. Rev. B 82, 205105 (2010).
  68. Y. Lu, M. Höppner, O. Gunnarsson, and M. W. Haverkort, Efficient real-frequency solver for dynamical mean-field theory, Phys. Rev. B 90, 085102 (2014).
  69. Y. Lu, X. Cao, P. Hansmann, and M. W. Haverkort, Natural-orbital impurity solver and projection approach for Green’s functions, Phys. Rev. B 100, 115134 (2019).
  70. J. M. Luttinger, Fermi surface and some simple equilibrium properties of a system of interacting fermions, Phys. Rev. 119, 1153 (1960).
  71. J. M. Luttinger, Analytic properties of single-particle propagators for many-fermion systems, Phys. Rev. 121, 942 (1961).
  72. M. Karski, C. Raas, and G. S. Uhrig, Single-particle dynamics in the vicinity of the Mott-Hubbard metal-to-insulator transition, Phys. Rev. B 77, 075116 (2008).
  73. M. Karski, C. Raas, and G. S. Uhrig, Electron spectra close to a metal-to-insulator transition, Phys. Rev. B 72, 113110 (2005).
  74. D. Zgid and E. Gull, Finite temperature quantum embedding theories for correlated systems, New J. Phys. 19, 023047 (2017).
  75. P. Werner, E. Gull, M. Troyer, and A. J. Millis, Spin freezing transition and non-Fermi-liquid self-energy in a three-orbital model, Phys. Rev. Lett. 101, 166405 (2008).
  76. L. de’ Medici, J. Mravlje, and A. Georges, Janus-faced influence of Hund’s rule coupling in strongly correlated materials, Phys. Rev. Lett. 107, 256401 (2011).
  77. A. Georges, L. de’ Medici, and J. Mravlje, Strong correlations from hund’s coupling, Annu. Rev. Condens. Matter Phys. 4, 137 (2013).
  78. M. Vojta, Orbital-selective Mott transitions: Heavy fermions and beyond, J. Low Temp. Phys. 161, 203 (2010).
  79. A. Koga, N. Kawakami, T. M. Rice, and M. Sigrist, Orbital-selective Mott transitions in the degenerate Hubbard model, Phys. Rev. Lett. 92, 216402 (2004).
  80. R. Arita and K. Held, Orbital-selective Mott-Hubbard transition in the two-band Hubbard model, Phys. Rev. B 72, 201102(R) (2005).
  81. L. de’ Medici, A. Georges, and S. Biermann, Orbital-selective Mott transition in multiband systems: Slave-spin representation and dynamical mean-field theory, Phys. Rev. B 72, 205124 (2005).
  82. R. K. Bryan, Maximum entropy analysis of oversampled data problems, Eur. Biophys. J. 18, 165 (1990).

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