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Ehrenfest dynamics with spontaneous localization.

Anderson A Tomaz1, Rafael S Mattos1, Saikat Mukherjee2

  • 1Aix Marseille University, CNRS, ICR, 13397 Marseille, France.

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|December 4, 2025
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Summary
This summary is machine-generated.

We introduce Ehrenfest Dynamics with Spontaneous Localization (SLED), a new method for quantum dynamics. SLED offers a physically consistent way to model decoherence in mixed quantum-classical systems.

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Area of Science:

  • Quantum dynamics
  • Theoretical chemistry
  • Computational physics

Background:

  • Mixed quantum-classical methods are essential for simulating chemical processes.
  • Existing methods often use ad hoc decoherence corrections, lacking physical rigor.
  • There is a need for a consistent theoretical framework to address decoherence.

Purpose of the Study:

  • To develop a decoherence-corrected extension of Ehrenfest dynamics.
  • To provide a physically consistent alternative to current mixed quantum-classical methods.
  • To establish a rigorous and extensible framework for quantum dynamics.

Main Methods:

  • Propose Ehrenfest Dynamics with Spontaneous Localization (SLED), based on the Gisin-Percival quantum-state diffusion equation.
  • The electronic wave function evolves stochastically in the adiabatic energy basis, leading to trajectory-level localization.
  • The ensemble of trajectories reproduces Lindblad-type propagation of the reduced electronic density matrix, ensuring linearity, trace preservation, and complete positivity.

Main Results:

  • SLED successfully reproduces electronic populations in benchmark simulations.
  • The method captures essential features of coherence decay in Tully models and spin-boson Hamiltonians.
  • Generalizing the localization kernel is identified as crucial for accurate electron-nucleus coupling treatment.

Conclusions:

  • SLED provides a rigorous and extensible framework for mixed quantum-classical dynamics.
  • It bridges the gap between mixed quantum-classical methods and open quantum system theory.
  • Future work will focus on refining the localization kernel for improved accuracy.