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

42.4K
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.
42.4K
Laminar Flow: Problem Solving01:24

Laminar Flow: Problem Solving

188
Laminar flow occurs when a fluid moves smoothly in parallel layers with minimal mixing and turbulence. In fluid mechanics, ensuring laminar flow within a pipe is essential for precise control of flow characteristics, especially in engineering applications. The key factor in determining whether flow remains laminar is the Reynolds number, a dimensionless quantity that depends on the fluid's velocity, density, viscosity, and the pipe's diameter. A Reynolds number of 2100 or lower...
188
Equilibrium Conditions for a Particle01:23

Equilibrium Conditions for a Particle

1.2K
When an object is in equilibrium, it is either at rest or moving with a constant velocity. There are two types of equilibrium: static and dynamic. Static equilibrium occurs when an object is at rest, while dynamic equilibrium occurs when an object is moving with a constant velocity. In both cases, there must be a balance of forces acting on the object.
To understand the concept of equilibrium, let us first consider the forces acting on an object. When different forces act on an object, they can...
1.2K
Couette Flow01:22

Couette Flow

284
Couette flow represents the flow of fluid between two parallel plates, with one plate fixed and the other moving with a constant velocity. This configuration allows for a simplified analysis using the Navier-Stokes equations, which govern fluid motion under conditions of viscosity and incompressibility. For Couette flow, the assumptions include a steady, laminar, incompressible flow with a zero-pressure gradient in the flow direction. This flow type is beneficial for understanding shear-driven...
284
Uniform Depth Channel Flow: Problem Solving01:18

Uniform Depth Channel Flow: Problem Solving

66
To calculate the flow rate for a trapezoidal channel, first, identify the bottom width, side slope, and flow depth of the channel. The cross-sectional area (A) corresponding to the depth of flow (y), channel bottom width (B), and side slope (θ) is determined by:Next, calculate the wetted perimeter, which includes the bottom width and the sloped side lengths in contact with the water. Using the values of the cross-sectional area and the wetted perimeter, determine the hydraulic radius by...
66
Turbulent Flow: Problem Solving01:09

Turbulent Flow: Problem Solving

137
Carbonation is a process used to dissolve carbon dioxide gas in a liquid, commonly used in the production of carbonated beverages. Achieving efficient carbonation requires careful control of temperature, pressure, and flow conditions. By adjusting these parameters, carbonation efficiency can be maximized, producing a higher concentration of CO2 in the liquid.
Temperature is a key factor in CO2 solubility. In this case, the CO2 gas and the liquid are cooled to 20°C. Lower temperatures...
137

You might also read

Related Articles

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

Sort by
Same author

Fermionic mean-field dynamics for spin systems beyond free fermions.

The Journal of chemical physics·2026
Same author

Atom-specific vibrational analysis reveals labile bonds in linear and branched PFOA molecules.

The Journal of chemical physics·2026
Same author

Quantum Electrodynamics Coupled-Cluster at Scale: High-Performance Implementation for Complex Systems.

Journal of chemical theory and computation·2025
Same author

A Perspective on Quantum Computing Applications in Quantum Chemistry Using 25-100 Logical Qubits.

Journal of chemical theory and computation·2025
Same author

Perspective on Many-Body Methods for Molecular Polaritonic Systems.

Journal of chemical theory and computation·2025
Same author

Qubit-Efficient Quantum Chemistry with the ADAPT Variational Quantum Eigensolver and Double Unitary Downfolding.

Journal of chemical theory and computation·2025
Same journal

Erratum: Bacterial Turbulence at Compressible Fluid Interfaces [Phys. Rev. Lett. 136, 138301 (2026)].

Physical review letters·2026
Same journal

Unveiling Light-Quark Yukawa Flavor Structure via Dihadron Fragmentation at Lepton Colliders.

Physical review letters·2026
Same journal

Adaptable Route to Fast Coherent State Transport via Bang-Bang-Bang Protocols.

Physical review letters·2026
Same journal

Topological Transition and Emergence of Elasticity of Dislocation in Skyrmion Lattice: Beyond Kittel's Magnetic-Polar Analogy.

Physical review letters·2026
Same journal

Pound-Drever-Hall Method for Superconducting-Qubit Readout.

Physical review letters·2026
Same journal

Coupling a ^{73}Ge Nuclear Spin to an Electrostatically Defined Quantum Dot in Silicon.

Physical review letters·2026
See all related articles

Related Experiment Video

Updated: Jul 9, 2025

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

583

Quantum Flow Algorithms for Simulating Many-Body Systems on Quantum Computers.

Karol Kowalski1, Nicholas P Bauman1

  • 1Physical Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99354, USA.

Physical Review Letters
|December 1, 2023
PubMed
Summary
This summary is machine-generated.

Quantum Flow (QFlow) simulations optimize wave function parameters for strongly correlated systems. This approach reduces circuit complexity, enabling scalable quantum computing with fewer parameters and qubits.

More Related Videos

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

9.7K
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

12.8K

Related Experiment Videos

Last Updated: Jul 9, 2025

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

583
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

9.7K
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

12.8K

Area of Science:

  • Quantum Computing
  • Computational Physics
  • Quantum Chemistry

Background:

  • Strongly correlated systems present significant challenges for classical and quantum simulations.
  • Efficiently sampling large Hilbert space subspaces is crucial for accurate quantum simulations.

Purpose of the Study:

  • To introduce and evaluate the Quantum Flow (QFlow) approach for quantum simulations.
  • To demonstrate QFlow's capability in reducing circuit complexity and optimizing parameters for strongly correlated systems.

Main Methods:

  • Utilized the Quantum Flow (QFlow) approach for quantum simulations.
  • Employed coupled variational problems in reduced dimensionality active spaces.
  • Performed simulations on strongly correlated systems.

Main Results:

  • QFlow enables sampling large Hilbert space subspaces via reduced dimensionality active spaces.
  • QFlow algorithms significantly reduce quantum circuit complexity.
  • Optimized wave function parameters without increasing qubit requirements, using fewer parameters.

Conclusions:

  • QFlow offers a pathway to scalable quantum computing with constant circuit depth.
  • The QFlow approach is effective for optimizing parameters in quantum simulations of complex systems.