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

Properties of the z-Transform I01:17

Properties of the z-Transform I

The z-transform is a fundamental tool in digital signal processing, enabling the analysis of discrete-time systems through its various properties. It is an invaluable tool for analyzing discrete-time systems, offering a range of properties that simplify complex signal manipulations. One fundamental property is linearity. For any two discrete-time signals, the z-transform of their linear combination equals the same linear combination of their individual z-transforms. This property is essential...
State Space Representation01:27

State Space Representation

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...
State Function, Exact and Inexact Differentials01:27

State Function, Exact and Inexact Differentials

A state function is a thermodynamic property that depends solely on the current state of a system, irrespective of its history or how it arrived at that state. These functions are represented by capital letters, such as U, H, and S, which stand for internal energy, enthalpy, and entropy, respectively.For instance, the value of internal energy depends on the system's state variables and remains unaffected by the process path. This means that whether the system underwent a linear process or a...
Vector Representation of Complex Numbers01:16

Vector Representation of Complex Numbers

Complex numbers, represented in Cartesian coordinates, can also be visualized as vectors. These vectors can be expressed in polar form, emphasizing their magnitude and angle. When a complex number is input into a function, the output is another complex number, highlighting the function's zero point from which the vector representation can originate.
Consider a function defined as the product of the complex factors in the numerator divided by the product of the complex factors in the denominator.
Vectors01:30

Vectors

Vectors are mathematical entities characterized by both magnitude and direction. Unlike scalars, which are defined solely by magnitude, vectors represent quantities like displacement, velocity, and force, where direction is essential. Vectors are graphically represented as directed line segments, extending from an initial point to a terminal point, denoted with bold letters or arrows placed above the symbol. Two vectors are deemed equal if they share identical magnitudes and directions,...
Convolution Properties I01:20

Convolution Properties I

Convolution computations can be simplified by utilizing their inherent properties.
The commutative property reveals that the input and the impulse response of an LTI (Linear Time-Invariant) system can be interchanged without affecting the output:

You might also read

Related Articles

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

Sort by
Same author

Cerebellar Time and Relative Time: A Comparator-Based Dynamical Timing Model and its Relevance to Psychopathology and Therapies.

Cerebellum (London, England)·2026
Same author

Generation of multiphoton Fock states by multiplexing heralded photon sources.

Optics express·2025
Same author

Even and Odd Cat States of Two and Three Qubits in the Probability Representation of Quantum Mechanics.

Entropy (Basel, Switzerland)·2024
Same author

Single-photon sources based on stepwise optimized binary-tree multiplexers.

Optics express·2024
Same author

Maximum information measurement for qubit states.

Scientific reports·2024
Same author

Implementing no-signaling correlations as a service.

Scientific reports·2024

Related Experiment Video

Updated: May 19, 2026

Generation and Coherent Control of Pulsed Quantum Frequency Combs
06:42

Generation and Coherent Control of Pulsed Quantum Frequency Combs

Published on: June 8, 2018

Variational properties of the discrete variable representation: discrete variable representation via effective

Viktor Szalay1, Péter Ádám

  • 1Institute for Solid State Physics and Optics, Wigner Research Centre for Physics, Hungarian Academy of Sciences, P. O. Box 49, H-1525 Budapest, Hungary. szalay.viktor@wigner.mta.hu

The Journal of Chemical Physics
|August 18, 2012
PubMed
Summary

A new variational finite basis representation/discrete variable representation (FBR/DVR) Hamiltonian operator was developed. This method enhances accuracy for quantum mechanical calculations by optimizing grid points and incorporating symmetry properties.

Related Experiment Videos

Last Updated: May 19, 2026

Generation and Coherent Control of Pulsed Quantum Frequency Combs
06:42

Generation and Coherent Control of Pulsed Quantum Frequency Combs

Published on: June 8, 2018

Area of Science:

  • Quantum mechanics
  • Computational chemistry
  • Theoretical physics

Background:

  • The finite basis representation/discrete variable representation (FBR/DVR) method is a powerful tool for solving quantum mechanical problems.
  • Accurate representation of the Hamiltonian operator is crucial for obtaining reliable results.
  • Existing FBR/DVR methods may have limitations in terms of accuracy and applicability to certain systems.

Purpose of the Study:

  • To introduce a novel variational finite basis representation/discrete variable representation (FBR/DVR) Hamiltonian operator.
  • To analyze the properties and optimize the performance of the proposed variational FBR/DVR method.
  • To demonstrate the applicability of the method to various quantum systems, including periodic ones.

Main Methods:

  • Development of a variational FBR/DVR Hamiltonian operator.
  • Exact calculation of matrix elements.
  • Diagonalization of commuting variational basis representations of coordinate operators.
  • Analysis of symmetry properties and incorporation into calculations.
  • Exploitation of quasi-Hermiticity for variational effective operators.

Main Results:

  • The introduced operator yields either a variational FBR or DVR depending on the basis set choice.
  • The domain of variational grid points is identified as subsets of points obtained from diagonalizing commuting operators.
  • The optimal subset of points for highest accuracy corresponds to the DVR with the smallest trace.
  • Symmetry properties are analyzed, and methods for their incorporation are discussed.
  • Fourier-basis FBR/DVR for periodic systems is derived within the presented theory.

Conclusions:

  • The developed variational FBR/DVR Hamiltonian operator offers enhanced accuracy and flexibility.
  • The method provides a systematic way to optimize grid points for improved computational efficiency.
  • The incorporation of symmetry and applicability to periodic systems broaden the scope of FBR/DVR methods.