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 Concept Videos

The Quantum-Mechanical Model of an Atom02:45

The Quantum-Mechanical Model of an Atom

47.1K
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...
47.1K
X-ray Diffraction of Biological Samples01:10

X-ray Diffraction of Biological Samples

3.8K
X-ray diffraction or XRD is an analytical tool that utilizes X-rays to study ordered structures such as crystalline organic and inorganic samples, polycrystalline materials, proteins, carbohydrates, and drugs.
According to Bragg's law, when X-rays strike the sample positioned on a stage, the rays are  scattered by the electron clouds around the sample atoms. The  X-ray diffraction or scattering is caused by constructive interference of the X-ray waves that reflect off the internal...
3.8K
One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

1.5K
This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...
1.5K

You might also read

Related Articles

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

Sort by
Same author

Mixed Conducting Oxide Coating for Lithium Batteries.

ACS nano·2024
Same author

First-principles calculations of bulk, surface and interfacial phases and properties of silicon graphite composites as anode materials for lithium ion batteries.

Physical chemistry chemical physics : PCCP·2022
See all related articles

Related Experiment Video

Updated: May 5, 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

14.1K

Evaluating Sample-Based Krylov Quantum Diagonalization for Heisenberg Models with Applications to Materials Science.

Neel Misciasci1,2, Roman Firt1, Jonathan E Mueller1

  • 1Volkswagen AG, Berliner Ring 2, 38440 Wolfsburg, Germany.

Entropy (Basel, Switzerland)
|May 4, 2026
PubMed
Summary

The Sample-based Krylov Quantum Diagonalization (SKQD) algorithm effectively analyzes Heisenberg models, even with complex ground states. This quantum method shows promise for simulating strongly correlated quantum systems accurately.

Keywords:
quantum computingquantum-centric supercomputing architecturessample-based quantum diagonalization methodsspin models

More Related Videos

Excitonic Hamiltonians for Calculating Optical Absorption Spectra and Optoelectronic Properties of Molecular Aggregates and Solids
08:04

Excitonic Hamiltonians for Calculating Optical Absorption Spectra and Optoelectronic Properties of Molecular Aggregates and Solids

Published on: May 27, 2020

7.5K
Probe Type II Band Alignment in One-Dimensional Van Der Waals Heterostructures Using First-Principles Calculations
13:56

Probe Type II Band Alignment in One-Dimensional Van Der Waals Heterostructures Using First-Principles Calculations

Published on: October 12, 2019

7.1K

Related Experiment Videos

Last Updated: May 5, 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

14.1K
Excitonic Hamiltonians for Calculating Optical Absorption Spectra and Optoelectronic Properties of Molecular Aggregates and Solids
08:04

Excitonic Hamiltonians for Calculating Optical Absorption Spectra and Optoelectronic Properties of Molecular Aggregates and Solids

Published on: May 27, 2020

7.5K
Probe Type II Band Alignment in One-Dimensional Van Der Waals Heterostructures Using First-Principles Calculations
13:56

Probe Type II Band Alignment in One-Dimensional Van Der Waals Heterostructures Using First-Principles Calculations

Published on: October 12, 2019

7.1K

Area of Science:

  • Quantum physics
  • Computational condensed matter physics

Background:

  • The Heisenberg model is a fundamental model in quantum magnetism.
  • Simulating strongly correlated quantum systems, especially those with non-sparse ground states, presents significant computational challenges.
  • The Sample-based Krylov Quantum Diagonalization (SKQD) algorithm is a novel approach for quantum system analysis.

Purpose of the Study:

  • To evaluate the performance and accuracy of the SKQD algorithm on one- and two-dimensional Heisenberg models.
  • To investigate SKQD's effectiveness in strongly correlated regimes with dense ground states.
  • To assess SKQD's applicability on quantum hardware.

Main Methods:

  • Application of the SKQD algorithm to Heisenberg models of varying dimensions.
  • Utilizing problem-informed initial states and magnetization sector sweeps.
  • Benchmarking SKQD results against Density Matrix Renormalization Group (DMRG) and exact diagonalization.
  • Implementation and testing of SKQD on actual quantum hardware (qubits).

Main Results:

  • SKQD accurately reproduces ground-state energies and field-dependent magnetization for Heisenberg models across different anisotropies.
  • Qualitative agreement was observed when compared to established methods like DMRG.
  • Accuracy of SKQD improves with increasing system anisotropy.
  • Successful demonstration of SKQD on 18- and 30-qubit quantum processors, yielding expected magnetization curves.
  • Effectiveness of SKQD extends to two-dimensional lattice systems, as indicated by simulations on a 64-qubit processor.

Conclusions:

  • The SKQD algorithm is a robust and effective method for studying quantum magnetism, particularly for Heisenberg models.
  • SKQD demonstrates reliable performance even for challenging problems with non-sparse ground states.
  • The algorithm shows scalability and applicability to both one- and two-dimensional quantum systems, including on current quantum hardware.