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

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. Schrödinger...
Modeling with Differential Equations01:25

Modeling with Differential Equations

Population dynamics can be described mathematically by considering the population size P(t) as a function of time. The rate of change of the population is then represented by the derivative of P(t). A simple assumption is that the rate of growth is proportional to the size of the population itself. This leads to an exponential growth model, where the population increases rapidly without bound. While this is a useful first approximation, it does not reflect realistic long-term...
Transmission-Line Differential Equations01:26

Transmission-Line Differential Equations

Transmission lines are essential components of electrical power systems. They are characterized by the distributed nature of resistance (R), inductance (L), and capacitance (C) per unit length. To analyze these lines, differential equations are employed to model the variations in voltage and current along the line.
Line Section Model
A circuit representing a line section of length Δx helps in understanding the transmission line parameters. The voltage V(x) and current i(x) are measured from the...
Introduction to Differential Equations01:20

Introduction to Differential Equations

A differential equation is a mathematical expression that establishes a relationship between a function and its derivatives. These equations are fundamental in modeling dynamic systems across various fields of science and engineering. The order of a differential equation is defined by the highest order derivative present in the equation. A first-order differential equation includes only the first derivative, while a second-order differential equation includes up to the second derivative of the...
Transfer Function to State Space01:23

Transfer Function to State Space

State-space representation is a powerful tool for simulating physical systems on digital computers, necessitating the conversion of the transfer function into state-space form. Consider an nth-order linear differential equation with constant coefficients, like those encountered in an RLC circuit. The state variables are selected as the output and its n−1 derivatives. Differentiating these variables and substituting them back into the original equation produces the state equations.
In an RLC...
The de Broglie Wavelength02:32

The de Broglie Wavelength

In the macroscopic world, objects that are large enough to be seen by the naked eye follow the rules of classical physics. A billiard ball moving on a table will behave like a particle; it will continue traveling in a straight line unless it collides with another ball, or it is acted on by some other force, such as friction. The ball has a well-defined position and velocity or well-defined momentum, p = mv, which is defined by mass m and velocity v at any given moment. This is the typical...

You might also read

Related Articles

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

Sort by
Same author

Gab1 but not Grb2 mediates tumor progression in Met overexpressing colorectal cancer cells.

Carcinogenesis·2008
Same author

Long-term donor-specific tolerance in rat cardiac allografts by intrabone marrow injection of donor bone marrow cells.

Transplantation·2008
Same author

Lsr2 of Mycobacterium tuberculosis is a DNA-bridging protein.

Nucleic acids research·2008
Same author

Amphetamine selectively enhances avoidance responding to a less salient stimulus in rats.

Journal of neural transmission (Vienna, Austria : 1996)·2008
Same author

Retrospective analysis of anterior correction and fusion for adolescent idiopathic thoracolumbar/lumbar scoliosis: the relationship between preserving mobile segments and trunk balance.

International orthopaedics·2008
Same author

Intrarenal antigens activate CD4+ cells via co-stimulatory signals from dendritic cells.

Journal of the American Society of Nephrology : JASN·2008

Related Experiment Videos

Simulation of quantum dynamics based on the quantum stochastic differential equation.

Ming Li1

  • 1School of Automation, Guangdong University of Technology, No. 100 Waihuan Xi Road, Guangzhou Higher Education Mega Center, Pan Yu District, Guangzhou, Guangdong, China. mingli4@mail.ustc.edu.cn

Thescientificworldjournal
|June 20, 2013
PubMed
Summary

This study introduces a new algorithm for simulating quantum state diffusion using quantum stochastic differential equations. The method accurately predicts the behavior of driven two-level systems, outperforming classical algorithms.

Related Experiment Videos

Area of Science:

  • Quantum Optics
  • Quantum Information Theory
  • Computational Physics

Background:

  • The Lindblad form quantum master equation is fundamental for describing open quantum systems.
  • Quantum state diffusion is a key concept in understanding the dynamics of quantum systems interacting with their environment.
  • Simulating complex quantum dynamics often requires efficient numerical methods.

Purpose of the Study:

  • To investigate quantum stochastic differential equations derived from the Lindblad form.
  • To develop a numerical simulation algorithm for the stochastic process of direct photodetection.
  • To predict the dynamical behavior of driven two-level systems with high accuracy.

Main Methods:

  • Derivation of quantum stochastic differential equations.
  • Formulation in terms of environment operators for quantum state diffusion.
  • Development of a numerical simulation algorithm for stochastic processes.
  • Comparison with the classical Runge-Kutta algorithm for performance analysis.

Main Results:

  • A general formulation for quantum state diffusion is presented.
  • A novel numerical algorithm for simulating direct photodetection is proposed.
  • The algorithm demonstrates superior accuracy and computational efficiency compared to classical methods.
  • The dynamical behavior of a driven two-level system is effectively predicted.

Conclusions:

  • The proposed algorithm offers an effective and superior method for simulating quantum dynamics.
  • This approach advances the study of quantum state diffusion and photodetection processes.
  • The findings have implications for quantum optics and quantum information processing.