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

1.3K
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 the...
1.3K
Neural Circuits01:25

Neural Circuits

3.1K
Neural circuits and neuronal pools are two of the main structures found in the nervous system. Neural circuits are networks of neurons that work together to carry out a specific task or process. They consist of interconnected neurons and glial cells, which provide structural and metabolic support.
Neuronal pools are collections of nerve cells with similar functions and interact through chemical and electrical signals. These pools include both interneurons (the central neural circuit nodes that...
3.1K
Maxwell-Boltzmann Distribution: Problem Solving01:20

Maxwell-Boltzmann Distribution: Problem Solving

3.1K
Individual molecules in a gas move in random directions, but a gas containing numerous molecules has a predictable distribution of molecular speeds, which is known as the Maxwell-Boltzmann distribution, f(v).
This distribution function f(v) is defined by saying that the expected number N (v1,v2) of particles with speeds between v1 and v2 is given by
3.1K
The Quantum-Mechanical Model of an Atom02:45

The Quantum-Mechanical Model of an Atom

60.7K
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.
60.7K
Ampere's Law: Problem-Solving01:31

Ampere's Law: Problem-Solving

4.4K
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...
4.4K
Biot-Savart Law: Problem-Solving00:59

Biot-Savart Law: Problem-Solving

4.0K
The magnitude and direction of a magnetic field created by a steady current can be calculated using the Biot-Savart law.
Consider a mobile phone battery bank as a source of steady current, which flows through the wire connected between the two. What is the magnitude of the magnetic field created by this current at a field point P?
To estimate the magnitude of the total magnetic field, we first consider a small current element of length dl, at a distance r from the field point. Now the following...
4.0K

You might also read

Related Articles

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

Sort by
Same author

Precise Quantum Chemistry calculations with few Slater Determinants.

Nature communications·2026
Same author

Predicting topological entanglement entropy in a Rydberg analogue simulator.

Nature physics·2025
Same author

Foundation neural-networks quantum states as a unified Ansatz for multiple hamiltonians.

Nature communications·2025
Same author

Spectroscopy of two-dimensional interacting lattice electrons using symmetry-aware neural backflow transformations.

Communications physics·2025
Same author

Acceleration without Disruption: DFT Software as a Service.

Journal of chemical theory and computation·2024
Same author

Ab-initio variational wave functions for the time-dependent many-electron Schrödinger equation.

Nature communications·2024

Related Experiment Video

Updated: Mar 7, 2026

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

1.2K

Solving the quantum many-body problem with artificial neural networks.

Giuseppe Carleo1, Matthias Troyer2,3

  • 1Theoretical Physics, ETH Zurich, 8093 Zurich, Switzerland. gcarleo@ethz.ch.

Science (New York, N.Y.)
|February 11, 2017
PubMed
Summary
This summary is machine-generated.

Machine learning simplifies the quantum many-body problem by learning wave functions. This approach accurately describes complex quantum systems, including interacting spins models.

More Related Videos

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
11:18

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

10.9K
Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology
09:44

Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology

Published on: March 8, 2024

6.0K

Related Experiment Videos

Last Updated: Mar 7, 2026

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

1.2K
Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
11:18

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

10.9K
Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology
09:44

Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology

Published on: March 8, 2024

6.0K

Area of Science:

  • Quantum Physics
  • Computational Physics
  • Machine Learning

Background:

  • The quantum many-body problem is computationally intensive due to the exponential complexity of wave functions.
  • Describing nontrivial correlations in quantum systems remains a significant challenge.

Purpose of the Study:

  • To demonstrate machine learning's capability in reducing the complexity of the quantum many-body problem.
  • To introduce a novel variational representation for quantum states using artificial neural networks.
  • To present a reinforcement-learning scheme for finding ground states and simulating time evolution.

Main Methods:

  • Utilizing artificial neural networks with a variable number of hidden neurons for variational representation.
  • Implementing a reinforcement-learning scheme to train the neural network.
  • Applying the method to prototypical interacting spins models in one and two dimensions.

Main Results:

  • Machine learning systematically reduces the complexity of the many-body wave function.
  • The reinforcement-learning scheme successfully identifies ground states.
  • The approach accurately describes the unitary time evolution of complex interacting quantum systems.
  • High accuracy was achieved for one- and two-dimensional interacting spins models.

Conclusions:

  • Machine learning offers a tractable computational approach to the quantum many-body problem.
  • The proposed neural network representation and reinforcement-learning scheme are effective for quantum system simulation.
  • This method shows promise for tackling complex quantum physics challenges.