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

Ampere-Maxwell's Law: Problem-Solving01:17

Ampere-Maxwell's Law: Problem-Solving

734
A parallel-plate capacitor with capacitance C, whose plates have area A and separation distance d, is connected to a resistor R and a battery of voltage V. The current starts to flow at t = 0. What is the displacement current between the capacitor plates at time t? From the properties of the capacitor, what is the corresponding real current?
To solve the problem, we can use the equations from the analysis of an RC circuit and Maxwell's version of Ampère's law.
For the first part of...
734
The Quantum-Mechanical Model of an Atom02:45

The Quantum-Mechanical Model of an Atom

42.9K
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.
42.9K
Ampere's Law: Problem-Solving01:31

Ampere's Law: Problem-Solving

3.7K
Ampere's law states that for any closed looped path, the line integral of the magnetic field along the path equals the vacuum permeability times the current enclosed in the loop. If the fingers of the right hand curl along the direction of the integration path, the current in the direction of the thumb is considered positive. The current opposite to the thumb direction is considered negative.
Specific steps need to be considered while calculating the symmetric magnetic field distribution...
3.7K
Woodward–Hoffmann Selection Rules and Microscopic Reversibility01:34

Woodward–Hoffmann Selection Rules and Microscopic Reversibility

3.3K
Electrocyclic reactions, cycloadditions, and sigmatropic rearrangements are concerted pericyclic reactions that proceed via a cyclic transition state. These reactions are stereospecific and regioselective. The stereochemistry of the products depends on the symmetry characteristics of the interacting orbitals and the reaction conditions. Accordingly, pericyclic reactions are classified as either symmetry-allowed or symmetry-forbidden. Woodward and Hoffmann presented the selection criteria for...
3.3K
Second-Order Circuits01:17

Second-Order Circuits

1.6K
Integrating two fundamental energy storage elements in electrical circuits results in second-order circuits, encompassing RLC circuits and circuits with dual capacitors or inductors (RC and RL circuits). Second-order circuits are identified by second-order differential equations that link input and output signals.
Input signals typically originate from voltage or current sources, with the output often representing voltage across the capacitor and/or current through the inductor. For example, in...
1.6K
First-Order Circuits01:15

First-Order Circuits

1.6K
First-order electrical circuits, which comprise resistors and a single energy storage element - either a capacitor or an inductor, are fundamental to many electronic systems. These circuits are governed by a first-order differential equation that describes the relationship between input and output signals.
One common example of a first-order circuit is the RC (resistor-capacitor) circuit. These circuits are used in relaxation oscillators such as neon lamp oscillator circuits. When voltage is...
1.6K

You might also read

Related Articles

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

Sort by
Same authorSame journal

CAFE: Cross-View Adaptive Fusion and Cluster Center Enhancement for Robust Multi-View Clustering.

IEEE transactions on pattern analysis and machine intelligence·2026
Same author

Dendrobine alleviates lung injury in septic mice by inhibiting mitochondrial-endoplasmic reticulum crosstalk-mediated NLRP3 inflammasome activation.

Journal of pharmacological sciences·2026
Same author

Adaptive kNN graph model.

Nature communications·2026
Same author

Comparative Prognostic Assessment of EKFC Equations for Mortality Prediction: A Population-based Cohort Study.

Cardiorenal medicine·2026
Same author

Local delivery of GPC3-CAR T cells in a thermosensitive hydrogel enhances antitumor efficacy in hepatocellular carcinoma.

International immunopharmacology·2026
Same author

HetDualCL: Dual-encoder contrastive learning for heterogeneous graphs.

Neural networks : the official journal of the International Neural Network Society·2026
Same journal

Relation DETR+: Exploring Explicit Position Relation Prior for Dense Prediction.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

RBF++: Quantifying and Optimizing Reasoning Boundaries across Measurable and Unmeasurable Capabilities for Chain-of-Thought Reasoning.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

DIVER: Reinforced Diffusion Breaks Imitation Bottlenecks in End-to-End Autonomous Driving.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

Ethics-Aware Safe Reinforcement Learning for Rare-Event Risk Control in Interactive Urban Driving.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

Learning Shape Anchors for Holistic Indoor Scene Understanding.

IEEE transactions on pattern analysis and machine intelligence·2026
See all related articles

Related Experiment Video

Updated: Aug 30, 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

645

Parameterized Hamiltonian Learning With Quantum Circuit.

Jinjing Shi, Wenxuan Wang, Xiaoping Lou

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |August 31, 2022
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces parameterized Hamiltonian learning (PHL), a quantum machine learning method for accurate quantum system determination. PHL successfully applied quantum computing to image segmentation, outperforming classical methods by removing manual intervention.

    More Related Videos

    Gradient Echo Quantum Memory in Warm Atomic Vapor
    10:00

    Gradient Echo Quantum Memory in Warm Atomic Vapor

    Published on: November 11, 2013

    12.9K
    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

    Related Experiment Videos

    Last Updated: Aug 30, 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

    645
    Gradient Echo Quantum Memory in Warm Atomic Vapor
    10:00

    Gradient Echo Quantum Memory in Warm Atomic Vapor

    Published on: November 11, 2013

    12.9K
    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

    Area of Science:

    • Quantum Computing
    • Quantum Machine Learning
    • Quantum Information Science

    Background:

    • Hamiltonian learning is crucial for accurately characterizing quantum systems.
    • Current methods may require manual intervention or lack efficiency for complex problems.

    Purpose of the Study:

    • To establish and explore the application of Parameterized Hamiltonian Learning (PHL) on quantum computers.
    • To demonstrate PHL's capability in solving practical problems like image segmentation.

    Main Methods:

    • Developed a parameterized quantum circuit by decomposing unitary operators.
    • Implemented an iterative PHL algorithm to optimize circuit parameters via gradient descent.
    • Conducted experiments on the Origin Pilot quantum computer.

    Main Results:

    • The PHL algorithm accurately performed image segmentation.
    • PHL eliminated the need for manual intervention, unlike the classical Grabcut algorithm.
    • Demonstrated the potential of quantum devices for practical applications.

    Conclusions:

    • PHL offers a novel quantum approach for accurate quantum system determination.
    • This technique provides a viable quantum solution for image segmentation and other complex problems.
    • Expands the application scope of quantum computing in real-world scenarios.