Predicting rate kernels via dynamic mode decomposition.

Area of Science:

• Quantum mechanics
• Computational physics
• Data-driven modeling

Background:

• Simulating open quantum systems presents significant computational challenges.
• Existing methods are often limited by high computational costs, especially for complex systems.
• Accurate simulation is crucial for understanding quantum phenomena.

Purpose of the Study:

• To investigate the application of Dynamic Mode Decomposition (DMD) for evaluating rate kernels in quantum rate processes.
• To assess DMD's effectiveness in reducing computational costs for quantum system simulations.
• To compare DMD's predictive accuracy against traditional propagation methods.

Main Methods:

• Utilized Dynamic Mode Decomposition (DMD), a data-driven model reduction technique.
• Characterized rate kernels using system snapshots from a limited time window.
• Performed simulations with and without external fields to evaluate DMD's robustness.

Main Results:

• DMD accurately predicts the long-term behaviors of open quantum systems.
• The method significantly reduces the computational cost compared to traditional propagation techniques.
• DMD's accuracy is maintained regardless of the presence of external fields.

Conclusions:

• Dynamic Mode Decomposition is a viable and efficient tool for simulating open quantum systems.
• DMD offers a powerful approach to overcome the computational limitations of traditional methods.
• This technique enables accurate predictions with reduced computational resources, broadening the scope of quantum system analysis.