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

55.5K
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.
55.5K
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

199
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
199

You might also read

Related Articles

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

Sort by
Same author

Photonic networking of quantum memories in high dimensions.

Science advances·2026
Same author

Mindful parenting and preschoolers' screen dependency behavior: the mediating role of parent-child relationship and the moderating role of effortful control.

Frontiers in psychology·2026
Same author

Putative buffering roles of two-way social support and psychological resilience in the association between nurse-patient conflict and situational emotional response: a cross-sectional correlational study among Chinese nursing interns.

BMC nursing·2026
Same author

Nurses' perceived trauma-informed climate and voice behavior for nursing care improvement: a moderated mediation model of psychological empowerment and cultural humility.

BMC nursing·2026
Same author

Quantum algorithm for simulating the wave equation.

Physical review. A·2026
Same author

Steering the digital transformation of service-oriented organizations by job crafting and innovative work behavior: employee-driven adaptability.

Frontiers in psychology·2026

Related Experiment Video

Updated: Dec 6, 2025

Experimental Methods for Trapping Ions Using Microfabricated Surface Ion Traps
11:45

Experimental Methods for Trapping Ions Using Microfabricated Surface Ion Traps

Published on: August 17, 2017

15.0K

Quantum approximate optimization of the long-range Ising model with a trapped-ion quantum simulator.

Guido Pagano1,2,3,4, Aniruddha Bapat5,2,3, Patrick Becker5,2,3

  • 1Joint Quantum Institute, University of Maryland and National Institute of Standards and Technology, College Park, MD 20742; monroe@umd.edu pagano@umd.edu.

Proceedings of the National Academy of Sciences of the United States of America
|October 7, 2020
PubMed
Summary

Researchers implemented a low-depth Quantum Approximate Optimization Algorithm (QAOA) on an analog quantum simulator. This quantum approach efficiently estimated ground-state energy for complex models, showing promise for optimization problems.

Keywords:
quantum algorithmsquantum computingquantum information sciencequantum simulationtrapped ions

More Related Videos

Cooling an Optically Trapped Ultracold Fermi Gas by Periodical Driving
11:21

Cooling an Optically Trapped Ultracold Fermi Gas by Periodical Driving

Published on: March 30, 2017

7.8K
Scalable Quantum Integrated Circuits on Superconducting Two-Dimensional Electron Gas Platform
05:39

Scalable Quantum Integrated Circuits on Superconducting Two-Dimensional Electron Gas Platform

Published on: August 2, 2019

10.1K

Related Experiment Videos

Last Updated: Dec 6, 2025

Experimental Methods for Trapping Ions Using Microfabricated Surface Ion Traps
11:45

Experimental Methods for Trapping Ions Using Microfabricated Surface Ion Traps

Published on: August 17, 2017

15.0K
Cooling an Optically Trapped Ultracold Fermi Gas by Periodical Driving
11:21

Cooling an Optically Trapped Ultracold Fermi Gas by Periodical Driving

Published on: March 30, 2017

7.8K
Scalable Quantum Integrated Circuits on Superconducting Two-Dimensional Electron Gas Platform
05:39

Scalable Quantum Integrated Circuits on Superconducting Two-Dimensional Electron Gas Platform

Published on: August 2, 2019

10.1K

Area of Science:

  • Quantum Computing
  • Quantum Simulation
  • Computational Physics

Background:

  • Quantum computers offer potential advantages for complex problems intractable for classical computers.
  • Quantum many-body systems require advanced simulation techniques.
  • Optimization and satisfiability problems are key areas for quantum advantage.

Purpose of the Study:

  • To implement a low-depth Quantum Approximate Optimization Algorithm (QAOA) using an analog quantum simulator.
  • To estimate the ground-state energy of the Transverse Field Ising Model with tunable long-range interactions.
  • To optimize combinatorial classical problems by sampling QAOA outputs.

Main Methods:

  • Utilized an analog quantum simulator with up to 40 trapped-ion qubits.
  • Employed high-fidelity, single-shot, individual qubit measurements for QAOA output sampling.
  • Executed the algorithm using both exhaustive search and closed-loop optimization of variational parameters.
  • Benchmarked experimental results against bootstrapping heuristic methods.

Main Results:

  • Successfully estimated ground-state energy for the Transverse Field Ising Model.
  • Demonstrated that QAOA performance does not degrade significantly with increasing system size.
  • Observed that QAOA runtime is largely independent of the number of qubits.
  • Achieved results consistent with numerical simulations.

Conclusions:

  • The implemented low-depth QAOA is effective for estimating ground-state energies and optimizing combinatorial problems.
  • Scalability of the QAOA approach appears robust, with performance and runtime showing favorable characteristics.
  • A comprehensive error analysis was performed, crucial for future advancements in QAOA applications.