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

56.2K
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.
56.2K
Neural Circuits01:25

Neural Circuits

2.5K
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...
2.5K
State Space Representation01:27

State Space Representation

483
The frequency-domain technique, commonly used in analyzing and designing feedback control systems, is effective for linear, time-invariant systems. However, it falls short when dealing with nonlinear, time-varying, and multiple-input multiple-output systems. The time-domain or state-space approach addresses these limitations by utilizing state variables to construct simultaneous, first-order differential equations, known as state equations, for an nth-order system.
Consider an RLC circuit, a...
483
Propagation of Action Potentials01:23

Propagation of Action Potentials

8.6K
The propagation of an action potential refers to the process by which a nerve impulse, or "action potential," travels along a neuron.
Neurons (nerve cells) have a resting membrane potential, with a slightly negative charge inside compared to outside. This is maintained by ion channels, such as sodium (Na+) and potassium (K+) channels, which control the flow of ions. When a stimulus, like a touch or a signal from another neuron, triggers the neuron, sodium channels open, allowing sodium ions to...
8.6K
State Space to Transfer Function01:21

State Space to Transfer Function

518
The conversion of state-space representation to a transfer function is a fundamental process in system analysis. It provides a method for transitioning from a time-domain description to a frequency-domain representation, which is crucial for simplifying the analysis and design of control systems.
The transformation process begins with the state-space representation, characterized by the state equation and the output equation. These equations are typically represented as:
518
Neuronal Communication01:28

Neuronal Communication

2.8K
Neurons, the fundamental units of the brain and nervous system, communicate through complex electrochemical signals that underpin all cognitive and bodily functions. This communication is primarily facilitated by a process involving the generation and propagation of an action potential along the axon of the neuron. When the internal electrical charge of a neuron surpasses a certain threshold, an action potential is triggered. This rapid change in voltage travels swiftly along the axon to the...
2.8K

You might also read

Related Articles

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

Sort by
Same author

Dressed-State Hamiltonian Engineering in a Strongly Interacting Solid-State Spin Ensemble.

Physical review letters·2026
Same author

Resource-state quantum RAM for fast and error-correctable queries.

Nature communications·2026
Same author

Magnon hydrodynamics in an atomically thin ferromagnet.

Science (New York, N.Y.)·2026
Same author

Ferrimagnetism of ultracold fermions in a multiband Hubbard system.

Science (New York, N.Y.)·2026
Same author

Subconjunctival Triamcinolone Acetonide Injection Compared with Dexamethasone Ophthalmic Insert for Inflammation Prophylaxis After Cataract Surgery: A Comparative Clinical Study.

Clinical ophthalmology (Auckland, N.Z.)·2026
Same author

Management of Post-Operative Inflammation After Cataract Surgery with Intracanalicular Dexamethasone Implant and Topical Ketorolac.

Clinical ophthalmology (Auckland, N.Z.)·2026

Related Experiment Video

Updated: Jan 1, 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

1.0K

Integrating Neural Networks with a Quantum Simulator for State Reconstruction.

Giacomo Torlai1,2,3, Brian Timar4, Evert P L van Nieuwenburg4

  • 1Center for Computational Quantum Physics, Flatiron Institute, New York, New York 10010, USA.

Physical Review Letters
|December 24, 2019
PubMed
Summary
This summary is machine-generated.

We reconstructed quantum many-body states from experimental data using a neural network and error mitigation. This approach extracts complex quantum information from programmable quantum simulators, enhancing future quantum hardware integration.

More Related Videos

Quantum State Engineering of Light with Continuous-wave Optical Parametric Oscillators
09:23

Quantum State Engineering of Light with Continuous-wave Optical Parametric Oscillators

Published on: May 30, 2014

14.9K
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

10.2K

Related Experiment Videos

Last Updated: Jan 1, 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

1.0K
Quantum State Engineering of Light with Continuous-wave Optical Parametric Oscillators
09:23

Quantum State Engineering of Light with Continuous-wave Optical Parametric Oscillators

Published on: May 30, 2014

14.9K
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

10.2K

Area of Science:

  • Quantum Information Science
  • Machine Learning in Physics
  • Quantum Simulation

Background:

  • Quantum many-body state reconstruction is crucial for understanding complex quantum systems.
  • Experimental data from quantum simulators often contain errors that obscure true quantum states.

Purpose of the Study:

  • To develop a method for reconstructing quantum many-body states from experimental data.
  • To integrate machine learning with quantum hardware for enhanced state reconstruction.

Main Methods:

  • Utilized a neural-network model, specifically restricted Boltzmann machine wave functions.
  • Applied a novel regularization technique to mitigate measurement errors in training data.
  • Extracted wave functions from a Rydberg quantum simulator with 8-9 atoms.

Main Results:

  • Successfully reconstructed quantum many-body states from experimental data.
  • Captured one- and two-body observables beyond experimental accessibility.
  • Computed sophisticated observables like Rényi mutual information.

Conclusions:

  • Demonstrated the efficacy of neural networks for quantum state reconstruction.
  • Showcased a method to mitigate experimental errors in quantum simulation data.
  • Paved the way for integrating machine learning with intermediate-scale quantum hardware.