Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Log inSign up
Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Blog
Job Board
Q3AS

© 2026 Aqora Quantum S.A.S.

TermsPrivacyLegal Notice

Accelerating Fermionic Molecular Dynamics

Michael A. Clark F G, Anthony D. Kennedy

Published Jan 1, 2004• 8 citations
OpenAlex

Abstract

We consider how to accelerate fermionic molecular dynamics algorithms by introducing n pseudofermion fields coupled with the nth root of the fermionic kernel. This reduces the maximum pseudofermionic force, and thus allows a larger molecular dynamics integration step size without hitting an instability in the integrator. 1.

Related papers

Quantum-Accelerated Molecular Simulation with Hybrid Density Functional Theory (HDFT)

Jincheng Zhang

Quantum-Accelerated Molecular Simulation with Hybrid Density Functional Theory (HDFT) addresses the limitations of traditional molecular simulation by leveraging the strengths of quantum chemistry and density functional theory (DFT). This paper proposes a novel approach that integrates quantum algorithms directly into DFT calculations to significantly accelerate the understanding of molecular behavior. The core claim is that this hybrid method will produce highly accurate simulations of molecular properties, offering a fundamentally new approach to computational modeling. We detail the integration process, explore the benefits of the hybrid strategy, and present preliminary results demonstrating its potential for accelerating molecular dynamics simulations.

•Sep 3, 2026•0 citations

Quantum-Enhanced Diffusion for Molecular Dynamics

Jincheng Zhang

This paper presents a novel framework for molecular dynamics simulations that integrates quantum entanglement to accelerate the simulation process and enhance accuracy. Traditional molecular dynamics simulations are computationally demanding, particularly for large systems and long timescales. We propose a "Quantum-Accelerated Diffusion Algorithm" – a modified diffusion process utilizing quantum entanglement to dramatically reduce computational time and improve the fidelity of molecular dynamics simulations. The core mechanism involves establishing entangled states between system particles, allowing for instantaneous correlation during the diffusion process. We demonstrate the effectiveness of this approach through simulations of complex molecular systems, showcasing significant speedups and improved accuracy compared to conventional methods. This work contributes to a paradigm shift in molecular dynamics, offering a potentially transformative approach to modeling molecular behavior.

•Aug 31, 2026•0 citations

Hybrid molecular-dynamics algorithm for the numerical simulation of many-electron systems

Richard T. Scalettar, Douglas J. Scalapino, Robert L. Sugar +1 more

We present an algorithm for the numerical simulation of many electron systems, which is based upon the hybrid stochastic approach presently being used to study lattice gauge theory. A method for preconditioning the fermion matrices is described which significantly reduces the computing time needed in the simulation. Results are presented for the three-dimensional Hubbard model on lattices ranging in size from ${4}^{3}$ to ${10}^{3}$.

Physical review. B, Condensed matter•Dec 1, 1987•52 citations

Quantum Computing Molecular Dynamics Simulation

Jincheng Zhang

Quantum Computing Molecular Dynamics Simulation aims to accelerate molecular dynamics simulations and enhance the ability to model complex molecular structures and interactions, ultimately providing more accurate molecular property predictions. This paper explores the potential of leveraging quantum computing's unique characteristics to overcome limitations inherent in classical methods, resulting in significantly reduced computational complexity and improved simulation accuracy. We detail the core mechanism, focusing on how quantum algorithms can be employed to overcome the computational bottlenecks in molecular dynamics simulations.

•Sep 2, 2026•0 citations

GPU Accelerated Quantum Molecular Dynamics Simulation for Efficient Electron-Force-Field Model

Zhanbo Li, Yuhao Zhang, M. Li

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.

International Conference on Artificial Intelligence and Computer Information Technology (AICIT)•Sep 20, 2024•0 citations

Path integral molecular dynamics approximations of quantum canonical observables

Xin Huang, Petr Plecháč, Mattias Sandberg +1 more

Mean-field molecular dynamics based on path integrals is used to approximate canonical quantum observables for particle systems consisting of nuclei and electrons. A computational bottleneck is the Monte Carlo sampling from the Gibbs density of the electron operator, which due to the fermion sign problem has a computational complexity that scales exponentially with the number of electrons. In this work, we construct an algorithm that approximates the mean-field Hamiltonian by path integrals for fermions. The algorithm is based on the determinant of a matrix with components built on Brownian bridges connecting permuted electron coordinates. The computational work for n electrons is O ( n 3 ) , which reduces the computational complexity associated with the fermion sign problem. We analyze a bias resulting from this approximation and provide a rough computational error indicator. It remains to rigorously explain the surprisingly high accuracy for high temperatures. The method becomes infeasible at low temperatures due to a large sample variance.

arXiv:2311.17333•Journal of Computational Physics•Nov 28, 2024•1 citation
Quantum-Accelerated Molecular Simulation with Hybrid Density Functional Theory (HDFT) addresses the limitations of traditional molecular simulation by leveraging the strengths of quantum chemistry and density functional theory (DFT). This paper proposes a novel approach that integrates quantum algorithms directly into DFT calculations to significantly accelerate the understanding of molecular behavior. The core claim is that this hybrid method will produce highly accurate simulations of molecular properties, offering a fundamentally new approach to computational modeling. We detail the integration process, explore the benefits of the hybrid strategy, and present preliminary results demonstrating its potential for accelerating molecular dynamics simulations.
This paper presents a novel framework for molecular dynamics simulations that integrates quantum entanglement to accelerate the simulation process and enhance accuracy. Traditional molecular dynamics simulations are computationally demanding, particularly for large systems and long timescales. We propose a "Quantum-Accelerated Diffusion Algorithm" – a modified diffusion process utilizing quantum entanglement to dramatically reduce computational time and improve the fidelity of molecular dynamics simulations. The core mechanism involves establishing entangled states between system particles, allowing for instantaneous correlation during the diffusion process. We demonstrate the effectiveness of this approach through simulations of complex molecular systems, showcasing significant speedups and improved accuracy compared to conventional methods. This work contributes to a paradigm shift in molecular dynamics, offering a potentially transformative approach to modeling molecular behavior.
We present an algorithm for the numerical simulation of many electron systems, which is based upon the hybrid stochastic approach presently being used to study lattice gauge theory. A method for preconditioning the fermion matrices is described which significantly reduces the computing time needed in the simulation. Results are presented for the three-dimensional Hubbard model on lattices ranging in size from 43{4}^{3}43 to 103{10}^{3}103.
Quantum Computing Molecular Dynamics Simulation aims to accelerate molecular dynamics simulations and enhance the ability to model complex molecular structures and interactions, ultimately providing more accurate molecular property predictions. This paper explores the potential of leveraging quantum computing's unique characteristics to overcome limitations inherent in classical methods, resulting in significantly reduced computational complexity and improved simulation accuracy. We detail the core mechanism, focusing on how quantum algorithms can be employed to overcome the computational bottlenecks in molecular dynamics simulations.
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.
Mean-field molecular dynamics based on path integrals is used to approximate canonical quantum observables for particle systems consisting of nuclei and electrons. A computational bottleneck is the Monte Carlo sampling from the Gibbs density of the electron operator, which due to the fermion sign problem has a computational complexity that scales exponentially with the number of electrons. In this work, we construct an algorithm that approximates the mean-field Hamiltonian by path integrals for fermions. The algorithm is based on the determinant of a matrix with components built on Brownian bridges connecting permuted electron coordinates. The computational work for n electrons is O ( n 3 ) , which reduces the computational complexity associated with the fermion sign problem. We analyze a bias resulting from this approximation and provide a rough computational error indicator. It remains to rigorously explain the surprisingly high accuracy for high temperatures. The method becomes infeasible at low temperatures due to a large sample variance.