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Variational adaptive Gaussian decomposition: Scalable quadrature-free time-sliced thawed Gaussian dynamics
1Department of Chemical Sciences, Tata Institute of Fundamental Research, Mumbai 400005, India.
The Journal of Chemical Physics
|August 6, 2026
Summary
We developed a new method called variational adaptive Gaussian decomposition (VAGD) to improve quantum dynamics simulations. This approach efficiently decomposes wave functions, enabling accurate semiclassical quantum dynamics calculations.
Area of Science:
- Quantum mechanics
- Computational chemistry
- Theoretical physics
Background:
- Time-slicing is crucial for semiclassical propagation in real-time path integral formulations.
- Decomposing time-evolved wave functions into Gaussian wave packets (GWPs) is a key step.
Purpose of the Study:
- Introduce a quadrature-free variational framework for GWP decomposition.
- Reformulate GWP decomposition as an optimization problem to maximize overlap with the wave function.
Main Methods:
- Employ an autoencoder-decoder neural network for GWP parameter optimization.
- Adaptively reoptimize the GWP representation during propagation.
- Utilize variational adaptive Gaussian decomposition (VAGD) for compact Gaussian expansion.
Main Results:
- VAGD provides a scalable route to time-sliced semiclassical quantum dynamics.
- Each GWP represents a localized patch of the semiclassical manifold with full correlations.
- VAGD applied to thawed Gaussian dynamics systematically improves semiclassical treatment.
Conclusions:
- VAGD offers a robust and efficient method for quantum dynamics simulations.
- This approach bridges semiclassical and full quantum mechanical results.
- VAGD enhances the accuracy and scalability of quantum dynamics calculations.
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