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

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

226
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...
226
Ampere-Maxwell's Law: Problem-Solving01:17

Ampere-Maxwell's Law: Problem-Solving

1.0K
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...
1.0K
Woodward–Hoffmann Selection Rules and Microscopic Reversibility01:34

Woodward–Hoffmann Selection Rules and Microscopic Reversibility

3.7K
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...
3.7K
Machines: Problem Solving II01:30

Machines: Problem Solving II

582
Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. Consider a lifting tong carrying a 100 kg load. It comprises movable sections DAF and CBG linked together with member AB.
582
Machines: Problem Solving I01:22

Machines: Problem Solving I

626
A toggle clamp is a mechanical device commonly used for holding and clamping objects in various applications, such as woodworking, metalworking, and assembly operations. Consider a toggle clamp subjected to a force of 200 N at the handle. The vertical clamping force can be calculated, provided the dimensions of the toggle clamp are known.
The toggle clamp system is a machine structure consisting of movable, pin-connected multi-force members that form a stabilized system to transmit forces. The...
626
Sequence Networks of Rotating Machines01:24

Sequence Networks of Rotating Machines

424
A Y-connected synchronous generator, grounded through a neutral impedance, is designed to produce balanced internal phase voltages with only positive-sequence components. The generator's sequence networks include a source voltage that is exclusively in the positive-sequence network. The sequence components of line-to-ground voltages at the generator terminals illustrate this configuration.
Zero-sequence current induces a voltage drop across the generator's neutral impedance and other...
424

You might also read

Related Articles

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

Sort by
Same author

Thouless quantum walks in topological flat bands.

Light, science & applications·2026
Same author

Phase retrieval via gain-based photonic XY-Hamiltonian optimization.

Communications physics·2026
Same author

Observation of Lump Solitons.

Physical review letters·2026
Same author

A large-scale reconfigurable multiplexed quantum photonic network.

Nature photonics·2026
Same author

Unwrapping photonic reservoirs: Enhanced expressivity via random Fourier encoding over stretched domains.

Chaos (Woodbury, N.Y.)·2025
Same author

Ising Machine by Dimensional Collapse of Nonlinear Polarization Oscillators.

Physical review letters·2025
Same journal

Long-term stabilization of intensity-difference squeezing from four-wave mixing in rubidium vapor.

Optics express·2026
Same journal

Robust 3D topography measurement of large-range high-aspect-ratio structures based on dual-domain statistical filtering in SD-OCT.

Optics express·2026
Same journal

Broadband transmissive terahertz metasurface for simultaneous quad-mode OAM multiplexing.

Optics express·2026
Same journal

Leveraging two-dimensional materials for high-sensitivity optical sensors: quasi-bound states in the continuum within hybrid metasurfaces.

Optics express·2026
Same journal

Resolution investigation for dual-spherical-wave optical scanning holographic microscopy: methods and performance.

Optics express·2026
Same journal

Robustness of parallel subnetwork-filtered diffractive deep neural networks.

Optics express·2026
See all related articles

Related Experiment Video

Updated: Dec 21, 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

996

Programming multi-level quantum gates in disordered computing reservoirs via machine learning.

Giulia Marcucci, Davide Pierangeli, Pepijn W H Pinkse

    Optics Express
    |May 15, 2020
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces TensorFlow for designing quantum gates in quantum information systems. Machine learning with TensorFlow effectively creates complex multi-level quantum gates using optical reservoirs.

    More Related Videos

    Nanofabrication of Gate-defined GaAs/AlGaAs Lateral Quantum Dots
    15:47

    Nanofabrication of Gate-defined GaAs/AlGaAs Lateral Quantum Dots

    Published on: November 1, 2013

    16.8K
    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 21, 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

    996
    Nanofabrication of Gate-defined GaAs/AlGaAs Lateral Quantum Dots
    15:47

    Nanofabrication of Gate-defined GaAs/AlGaAs Lateral Quantum Dots

    Published on: November 1, 2013

    16.8K
    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 Information Science
    • Machine Learning
    • Quantum Computing

    Background:

    • Novel machine learning tools offer new possibilities for quantum information systems.
    • Designing multi-level quantum gates is crucial for advancing quantum computation.

    Purpose of the Study:

    • To utilize the TensorFlow programming library for designing multi-level quantum gates.
    • To investigate the use of trainable input and readout operators for gate realization.
    • To explore the application of these methods in optical systems with complex media.

    Main Methods:

    • Adoption of the open-source TensorFlow library.
    • Representation of the computing reservoir using random unitary matrices, applicable to optical disordered media or multi-modal fibers.
    • Implementation of trainable operators for input and readout control.

    Main Results:

    • Demonstration of successful design of multi-level quantum gates, including various qudit gates.
    • Analysis of the scaling properties of the algorithms with reservoir size.
    • Validation of TensorFlow's versatility in designing gates for complex media, including spatial light modulators.

    Conclusions:

    • TensorFlow is a powerful and versatile tool for designing complex quantum gates.
    • Trainable operators are effective in realizing multi-level gates within optical systems.
    • The developed methods show promise for advancing quantum information processing.