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Chain-mapping and tensor-network methods for open quantum systems are improved by replacing complex fermionic environments with simpler ones. This reduces computational time for simulating long-time dynamics.

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Area of Science:

  • Quantum Physics
  • Computational Chemistry
  • Condensed Matter Physics

Background:

  • Chain-mapping and tensor-network techniques offer numerically exact simulations of open quantum systems.
  • These methods face challenges with quadratic time scaling, especially for multiple or highly correlated fermionic environments.
  • Simulating long-time dynamics in such systems is computationally demanding.

Purpose of the Study:

  • To develop more efficient simulation methods for open quantum systems with structured environments.
  • To address the computational cost associated with fermionic environments and multiple environmental interactions.
  • To reduce the time complexity of chain-mapping based algorithms for long-time dynamics.

Main Methods:

  • Thermo-chemical modulation of spectral density to simplify fermionic environments.
  • Development and application of the fermionic Markovian closure construction.
  • Utilizing a reduced set of damped fermionic modes with Lindblad-type dynamics.

Main Results:

  • Thermo-chemical modulation successfully replaces complex fermionic environments with simpler, equivalent ones.
  • The proposed procedure reduces the number of chains required for modeling multiple environments.
  • The fermionic Markovian closure enables a polynomial reduction in time complexity for long-time simulations.

Conclusions:

  • The study presents a significant advancement in the efficient simulation of open quantum systems.
  • The developed methods offer a computationally tractable approach for handling complex fermionic environments.
  • This work paves the way for more extensive studies of quantum dynamics in challenging systems.