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arXiv:2608.14544

GPU implementation of mixed quantum-classical Liouville molecular dynamics without momentum jump

Koji Ando

physics.chem-phcond-mat.mtrl-sciphysics.comp-phquant-phSubmitted Aug 14, 2026• Announced Aug 17, 2026• 0 citations
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Abstract

We implemented on GPU a mixed quantum-classical Liouville molecular dynamics simulation based on a momentum-jump-free theory. The trajectory spawning that was previously implemented on CPU for sampling enhancement was eliminated to avoid the overhead of thread divergence and dynamic memory allocation on the GPU. This achieved a speedup of an order of magnitude compared to the CPU computation with spawning, as well as a linear scaling with respect to the number of sampling trajectories.

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4 pages, 5 figures

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Quantum computing simulation on a classical computer is difficult due to the exponential runtime and memory overhead. Previous work addresses the difficulty by utilizing multiple Graphical Processing Units (GPUs) and multi-node computers. GPUs are efficient for handling runtime issues but have limited total accessible memory space. Meanwhile, the memory of a multi-node computer can be scaled to the petabytes order, but its bandwidth for access from host computers (CPUs) is narrow. To simultaneously accelerate simulation and enlarge the total memory space, we propose a heterogeneous parallelization approach by combining GPUs and CPUs. Our simulator allocates memory to the GPUs first, and then to the CPUs. It thus accelerates simulation by using the full capabilities of the GPUs if memory for the simulation fits in the GPUs on a cluster. Allocating memory to the CPUs reduces benefits of the GPUs but enlarges the capacity of qubits in the simulation. In such case, it can exploit the memory of the GPUs to add one more qubit in the simulation if the size of memory in a node is the power of two (such as 512GB). We show empirical performance evaluations of our simulator in a distributed environment of POWER9.
Traditional molecular dynamics simulations have been providing crucial support to the fields of physics, chemistry, and materials science for many years. However, recent research efforts have increasingly focused on microphysical phenomena and quantum effects. Given the cost and complexity of experimental studies, quantum molecular dynamics simulations have emerged as a viable alternative for research. Despite their potential, existing simulation projects are hindered by the limited performance of CPUs. To overcome this limitation, we present an advanced and efficient implementation of Electron Field Force (EFF) model. The Electron Field Force (EFF) model, based on wave-particle duality and numerical approximation, can effectively provide an accurate correction to the conventional results derived from traditional methods. In this paper, a GPU-accelerated version of the EFF model is proposed to enhance simulation capabilities significantly. Our implementation has achieved a remarkable speedup of 90x on a single A100 GPU node. Additionally, we have developed a scalable multi-GPU version using MPI+CUDA technology for further scalability, which greatly extends the scope of research in quantum molecular dynamics.
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