Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Video

Updated: Jan 16, 2026

Multiscale Sampling of a Heterogeneous Water/Metal Catalyst Interface using Density Functional Theory and Force-Field Molecular Dynamics
10:52

Multiscale Sampling of a Heterogeneous Water/Metal Catalyst Interface using Density Functional Theory and Force-Field Molecular Dynamics

Published on: April 12, 2019

13.3K

Research on Quantum Circuit Learning Models for Molecular Dynamics Simulation.

Y Nishida1

  • 1Corporate Research & Development Center, Toshiba Corporation, 1 Komukai-Toshiba-cho, Saiwai-ku, Kawasaki 212-8582, Japan.

The Journal of Physical Chemistry. A
|September 26, 2025
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

The Quantum-Mechanical Model of an Atom02:45

The Quantum-Mechanical Model of an Atom

56.6K
Shortly after de Broglie published his ideas that the electron in a hydrogen atom could be better thought of as being a circular standing wave instead of a particle moving in quantized circular orbits, Erwin Schrödinger extended de Broglie’s work by deriving what is now known as the Schrödinger equation. When Schrödinger applied his equation to hydrogen-like atoms, he was able to reproduce Bohr’s expression for the energy and, thus, the Rydberg formula governing hydrogen spectra.
56.6K
Molecular Models02:00

Molecular Models

43.5K
Physical models representing molecular architectures of chemical compounds play essential roles in understanding chemistry. The use of molecular models makes it easier to visualize the structures and shapes of atoms and molecules.
43.5K
Electron Orbital Model01:18

Electron Orbital Model

71.7K
Orbitals are the areas outside of the atomic nucleus where electrons are most likely to reside. They are characterized by different energy levels, shapes, and three-dimensional orientations. The location of electrons is described most generally by a shell or principal energy level, then by a subshell within each shell, and finally, by individual orbitals found within the subshells.
The first shell is closest to the nucleus, and it has only one subshell with a single spherical orbital called the...
71.7K
Molecular Orbital Theory I02:35

Molecular Orbital Theory I

46.9K
Overview of Molecular Orbital Theory
46.9K
Predicting Molecular Geometry02:27

Predicting Molecular Geometry

45.2K
VSEPR Theory for Determination of Electron Pair Geometries
45.2K
Molecular Orbital Theory II03:51

Molecular Orbital Theory II

26.9K
Molecular Orbital Energy Diagrams
26.9K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same journal

Stability of Some Ternary 13-Atom Icosahedral Clusters Assessed with Geometric, Electronic, and Thermodynamic Criteria.

The journal of physical chemistry. A·2026
Same journal

A Three-Phase Distribution Method for Quantifying the Intermolecular Interactions.

The journal of physical chemistry. A·2026
Same journal

Cooperative Effects in the Inverse Coordination Complexes of Aromatic Azines and Tin(IV) Halides.

The journal of physical chemistry. A·2026
Same journal

The Infrared Spectra of Neutral Dimethyl-Sulfide, -Disulfide and -Sulfoxide Biomarkers in Molecular Beams.

The journal of physical chemistry. A·2026
Same journal

Photoinduced Charge-Transfer Suppresses Triplet Formation Efficiency in Thiocoumarins: Evidence from Ultrafast Spectroscopy and Theoretical Calculations.

The journal of physical chemistry. A·2026
Same journal

Porphyrin Aggregation Revisited: From the Four-Orbital Gouterman Model to an Eight-Orbital Framework in Porphin H-Dimers.

The journal of physical chemistry. A·2026
See all related articles

We developed a quantum circuit learning model for molecular dynamics simulations. This trained model efficiently estimates molecular energies, enabling faster simulations than traditional variational quantum algorithms.

Area of Science:

  • Quantum computing
  • Computational chemistry
  • Machine learning

Background:

  • Growing interest in Noisy Intermediate-Scale Quantum (NISQ) devices has led to hybrid quantum-classical algorithms in various fields.
  • Quantum chemistry studies often focus on precise eigenstate energy calculations.
  • Molecular dynamics simulations using quantum computers are underexplored due to limitations of variational quantum algorithms for long time steps.

Purpose of the Study:

  • Propose a quantum circuit learning model to estimate molecular Hamiltonian eigenvalues for arbitrary configurations.
  • Overcome the limitations of traditional variational approaches in molecular dynamics.

Main Methods:

  • Developed a quantum circuit learning model trained to estimate eigenvalues of a molecular Hamiltonian.

More Related Videos

Author Spotlight: Exploring Cellular Processes by Modeling Ligands in Cryo-EM Maps
09:30

Author Spotlight: Exploring Cellular Processes by Modeling Ligands in Cryo-EM Maps

Published on: July 19, 2024

2.0K
Author Spotlight: Streamlining Visual Dynamics to Simplify Molecular Dynamics Simulations Using Gromacs
05:00

Author Spotlight: Streamlining Visual Dynamics to Simplify Molecular Dynamics Simulations Using Gromacs

Published on: August 9, 2024

1.9K

Related Experiment Videos

Last Updated: Jan 16, 2026

Multiscale Sampling of a Heterogeneous Water/Metal Catalyst Interface using Density Functional Theory and Force-Field Molecular Dynamics
10:52

Multiscale Sampling of a Heterogeneous Water/Metal Catalyst Interface using Density Functional Theory and Force-Field Molecular Dynamics

Published on: April 12, 2019

13.3K
Author Spotlight: Exploring Cellular Processes by Modeling Ligands in Cryo-EM Maps
09:30

Author Spotlight: Exploring Cellular Processes by Modeling Ligands in Cryo-EM Maps

Published on: July 19, 2024

2.0K
Author Spotlight: Streamlining Visual Dynamics to Simplify Molecular Dynamics Simulations Using Gromacs
05:00

Author Spotlight: Streamlining Visual Dynamics to Simplify Molecular Dynamics Simulations Using Gromacs

Published on: August 9, 2024

1.9K
  • Once trained, the model eliminates the need for repeated variational optimization during molecular coordinate updates.
  • Applied the trained model to perform molecular dynamics simulations.

    • Main Results:

    • The trained quantum circuit learning model can estimate eigenvalues for given molecular configurations.
    • Demonstrated the model's capability in performing simple molecular dynamics simulations, including Langevin dynamics and NVE simulations.
    • Showcased applications inspired by classical machine learning techniques.

    Conclusions:

    • The proposed quantum circuit learning model offers an efficient alternative for molecular dynamics simulations.
    • This approach addresses the challenges of traditional variational methods in handling long time steps.
    • The model shows promise for broader applications in computational chemistry and materials science.