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paces: Parallelized application of co-evolving subspaces. A method for computing quantum dynamics on GPUs
R Kevin Kessing1,2,3
1Institut für Theoretische Physik, Universität Ulm, 89081 Ulm, Germany.
The Journal of Chemical Physics
|July 27, 2026
Summary
A new parallel algorithm efficiently solves the time-dependent Schrödinger equation by using dynamically updated subspaces. This method accurately computes quantum dynamics for complex molecular systems, including chromophore nanoaggregates.
Area of Science:
- Quantum mechanics
- Computational chemistry
- Materials science
Background:
- Solving the time-dependent Schrödinger equation is crucial for understanding quantum systems.
- Existing methods face challenges with computational cost and scalability for complex systems.
Purpose of the Study:
- To develop an efficient and scalable numerical method for solving the time-dependent Schrödinger equation.
- To enable accurate computation of quantum dynamics in complex molecular systems.
Main Methods:
- A novel algorithm constructs a restricted, dynamically evolving subspace of the Hilbert space.
- Time evolution is computed exactly within this subspace.
- The method is implemented as a parallel algorithm for graphics processing units (GPUs), optimized for sparse Hamiltonians.
Main Results:
- The method was benchmarked against established multiset-matrix-product state (MPS) results for a 1D Holstein model, showing high accuracy.
- The algorithm was applied to compute optical spectra and non-equilibrium dynamics of various dimensional model chromophore nanoaggregates.
Conclusions:
- This GPU-accelerated method provides an efficient and accurate approach for simulating quantum dynamics.
- It offers a powerful tool for studying light-harvesting systems and other complex quantum phenomena.
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