Iterative quantum-classical path integral with dynamically consistent state hopping.

Area of Science:

• Quantum chemistry
• Computational physics
• Chemical dynamics

Background:

• Iterative quantum-classical path integral (iQCPI) calculations are computationally intensive.
• Convergence in iQCPI is challenged by strong system-environment coupling and long memory effects.

Purpose of the Study:

• To improve the convergence and efficiency of iQCPI calculations.
• To reduce the computational cost associated with long memory kernels in quantum-classical simulations.

Main Methods:

• Investigated the impact of pre-memory trajectory branch selection on convergence.
• Introduced a dynamically consistent state hopping (DCSH) scheme.
• Implemented an instantaneous population-based probabilistic method for state-to-state hops.

Main Results:

• The choice of trajectory branch significantly affects required memory length.
• The reactant state trajectory branch improves convergence at short times.
• The DCSH scheme accelerates convergence and drastically reduces computational effort.

Conclusions:

• The DCSH scheme offers a significant computational advantage for iQCPI.
• Optimized trajectory selection is crucial for efficient quantum dynamics simulations.
• This method enhances the feasibility of simulating complex quantum systems in dissipative environments.