Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

Integration of Variational Quantum Algorithms into Atomistic Simulation Workflows

Wilke Dononelli

TL;DR

This work presents a workflow that couples Qiskit Nature's variational quantum chemistry solvers to the Atomic Simulation Environment, enabling hybrid quantum–classical simulations for geometry optimization, vibrational analysis, strain evaluation, and molecular dynamics. By employing ADAPT–VQE (and VQE with UCCSD references) within ASE, and leveraging on-the-fly active learning (FALCON) to stabilize MD, the authors demonstrate chemically meaningful forces and CCSD-like vibrational properties in minimal-basis calculations for H2, F2, BeH2, and H3+. The results show that adaptive quantum Ansätze significantly improve convergence and force stability over fixed ansätze, enabling practical force-driven quantum–classical atomistic modelling and paving the way for larger systems and embedding/Multiscale approaches as quantum hardware evolves. This integration provides a practical path toward widespread use of variational quantum algorithms within mainstream atomistic simulation workflows, with publicly accessible code and datasets to foster replication and extension.

Abstract

In this work, we present the integration of Qiskit Nature's quantum chemistry solvers into the Atomic Simulation Environment (ASE), enabling hybrid quantum-classical workflows for force-driven atomistic simulations. This coupling allows the use of the Variational Quantum Eigensolver (VQE) and its adaptive variant (ADAPT-VQE) not only for ground-state energy calculations, but also for geometry optimisation, vibrational frequency analysis, strain evaluation, and molecular dynamics, all managed through ASE's calculator interface. By applying ADAPT-VQE to multi-electron systems such as BeH2, we obtain vibrational and structural properties in close agreement with high-level classical CCSD calculations within the same minimal basis. These results demonstrate that adaptive variational quantum algorithms can deliver stable and chemically meaningful forces within an atomistic modelling workflow, enabling downstream applications such as molecular dynamics and active-learning accelerated simulations.

Integration of Variational Quantum Algorithms into Atomistic Simulation Workflows

TL;DR

This work presents a workflow that couples Qiskit Nature's variational quantum chemistry solvers to the Atomic Simulation Environment, enabling hybrid quantum–classical simulations for geometry optimization, vibrational analysis, strain evaluation, and molecular dynamics. By employing ADAPT–VQE (and VQE with UCCSD references) within ASE, and leveraging on-the-fly active learning (FALCON) to stabilize MD, the authors demonstrate chemically meaningful forces and CCSD-like vibrational properties in minimal-basis calculations for H2, F2, BeH2, and H3+. The results show that adaptive quantum Ansätze significantly improve convergence and force stability over fixed ansätze, enabling practical force-driven quantum–classical atomistic modelling and paving the way for larger systems and embedding/Multiscale approaches as quantum hardware evolves. This integration provides a practical path toward widespread use of variational quantum algorithms within mainstream atomistic simulation workflows, with publicly accessible code and datasets to foster replication and extension.

Abstract

In this work, we present the integration of Qiskit Nature's quantum chemistry solvers into the Atomic Simulation Environment (ASE), enabling hybrid quantum-classical workflows for force-driven atomistic simulations. This coupling allows the use of the Variational Quantum Eigensolver (VQE) and its adaptive variant (ADAPT-VQE) not only for ground-state energy calculations, but also for geometry optimisation, vibrational frequency analysis, strain evaluation, and molecular dynamics, all managed through ASE's calculator interface. By applying ADAPT-VQE to multi-electron systems such as BeH2, we obtain vibrational and structural properties in close agreement with high-level classical CCSD calculations within the same minimal basis. These results demonstrate that adaptive variational quantum algorithms can deliver stable and chemically meaningful forces within an atomistic modelling workflow, enabling downstream applications such as molecular dynamics and active-learning accelerated simulations.
Paper Structure (15 sections, 5 figures, 1 table)

This paper contains 15 sections, 5 figures, 1 table.

Table of Contents

  1. Introduction
  2. Computational Methods
  3. Results
  4. Proof of principle
  5. H2
  6. F2
  7. BeH2
  8. Advanced applications
  9. H3+: Global Optimization, Vibrations, and Strain Analysis
  10. Molecular Dynamics enhanced by on-the-fly trained Machine Learning Potential using VQE Energies and Forces
  11. Conclusion and Outlook
  12. Data Availability
  13. Author Contributions
  14. Competing Interests
  15. Acknowledgement

Figures (5)

  • Figure 1: Compact workflow of ASE--QiskitVQECalculator: ASE passes geometry to Qiskit Nature for VQE/ADAPT-VQE on a selected backend, returning energies, forces, and properties to ASE.
  • Figure 2: Vibrational modes and strain energies of H$_3^+$
  • Figure 3: Molecular dynamics simulations of H$_2$ using ADAPT--VQE forces combined with on-the-fly active learning (FALCON). Top: Total energy along a representative trajectory; red markers indicate time steps at which additional VQE force evaluations were performed for model retraining. Middle: Corresponding H--H bond distance as a function of time. Bottom: Comparison of four independent MD runs, annotated with the total number of VQE calculations required in each case.
  • Figure 4: Trajectory of molecular dynamics run of BeH$_2$ at room temperature. Black atoms are start positions, grey are intermediate positions. In white the H atoms and green the Be atoms of the final configuration are shown, respectively.
  • Figure :