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

52.1K
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.
52.1K
Maxwell-Boltzmann Distribution: Problem Solving01:20

Maxwell-Boltzmann Distribution: Problem Solving

1.8K
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
1.8K
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

115
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...
115
The Pauli Exclusion Principle03:06

The Pauli Exclusion Principle

53.3K
The arrangement of electrons in the orbitals of an atom is called its electron configuration. We describe an electron configuration with a symbol that contains three pieces of information:
53.3K
Quantum Numbers02:43

Quantum Numbers

43.8K
It is said that the energy of an electron in an atom is quantized; that is, it can be equal only to certain specific values and can jump from one energy level to another but not transition smoothly or stay between these levels.
43.8K
Equilibrium Conditions for a Particle01:23

Equilibrium Conditions for a Particle

1.6K
When an object is in equilibrium, it is either at rest or moving with a constant velocity. There are two types of equilibrium: static and dynamic. Static equilibrium occurs when an object is at rest, while dynamic equilibrium occurs when an object is moving with a constant velocity. In both cases, there must be a balance of forces acting on the object.
To understand the concept of equilibrium, let us first consider the forces acting on an object. When different forces act on an object, they can...
1.6K

You might also read

Related Articles

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

Sort by
Same author

Mapping the crystallization landscape of rare earth MOFs: a high-throughput investigation of structure, kinetics, and selectivity.

Chemical science·2026
Same author

Quantitative prediction of siRNA complexation by ionizable drugs enables their codelivery in nanoparticles.

Science advances·2026
Same author

Lessons From Drug Discovery for Cryoprotective Agent Design: An AI-Oriented Perspective.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)·2026
Same author

Combinatorial optimization enhanced by shallow quantum circuits with 104 superconducting qubits.

National science review·2026
Same author

Photochemical post-functionalization of polystyrene enables accelerated chemical recycling.

Chemical science·2026
Same author

Adsorption Hysteresis Under Control: Tuning Host-Guest Interactions via a Genetic Algorithm.

ACS nano·2026
Same journal

The TaMYB55-TaSnRK1α1-TabZIP9 module confers heat stress tolerance in wheat.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same journal

Superstatistics approach to turbulent circulation fluctuations.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same journal

A molecular timescale for evolution of cobamide biosynthesis.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same journal

Pierre Chambon, a pioneer of molecular biology and gene regulation in eukaryotes.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same journal

Granulosa cell glycogen fuels the avascular corpus luteum.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same journal

Synthetic essentiality of TRAIL/TNFSF10 in VHL-deficient renal cell carcinoma.

Proceedings of the National Academy of Sciences of the United States of America·2026
See all related articles

Related Experiment Video

Updated: Oct 6, 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

718

A quantum-quantum Metropolis algorithm.

Man-Hong Yung1, Alán Aspuru-Guzik

  • 1Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA 02138, USA.

Proceedings of the National Academy of Sciences of the United States of America
|January 5, 2012
PubMed
Summary
This summary is machine-generated.

A new quantum algorithm generalizes the classical Metropolis sampling method for both classical and quantum systems, offering a quantum speedup and avoiding issues with fermionic systems.

More Related Videos

Isotopic Effect in Double Proton Transfer Process of Porphycene Investigated by Enhanced QM/MM Method
05:51

Isotopic Effect in Double Proton Transfer Process of Porphycene Investigated by Enhanced QM/MM Method

Published on: July 19, 2019

6.3K
Silicon Metal-oxide-semiconductor Quantum Dots for Single-electron Pumping
14:58

Silicon Metal-oxide-semiconductor Quantum Dots for Single-electron Pumping

Published on: June 3, 2015

14.9K

Related Experiment Videos

Last Updated: Oct 6, 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

718
Isotopic Effect in Double Proton Transfer Process of Porphycene Investigated by Enhanced QM/MM Method
05:51

Isotopic Effect in Double Proton Transfer Process of Porphycene Investigated by Enhanced QM/MM Method

Published on: July 19, 2019

6.3K
Silicon Metal-oxide-semiconductor Quantum Dots for Single-electron Pumping
14:58

Silicon Metal-oxide-semiconductor Quantum Dots for Single-electron Pumping

Published on: June 3, 2015

14.9K

Area of Science:

  • Quantum Computing
  • Statistical Mechanics
  • Computational Chemistry

Background:

  • The classical Metropolis sampling method is widely used in statistical modeling for classical systems.
  • Simulating quantum systems classically is challenging due to difficulties in obtaining eigenstates of quantum Hamiltonians.
  • Existing quantum Monte Carlo methods face limitations like the negative-sign problem for fermionic systems.

Purpose of the Study:

  • To present a quantum algorithm that generalizes the classical Metropolis algorithm to the quantum domain.
  • To enable efficient simulation of arbitrary quantum systems.
  • To overcome limitations of classical methods and provide a quantum advantage.

Main Methods:

  • Development of a novel quantum algorithm based on the Metropolis sampling principle.
  • Leveraging quantum computation to efficiently sample thermal distributions of quantum systems.
  • Algorithm designed to be applicable to both classical and quantum systems.

Main Results:

  • The proposed algorithm offers a quantum speedup compared to its classical counterpart.
  • The algorithm successfully generalizes the Metropolis method to quantum systems.
  • It avoids the negative-sign problem encountered in quantum Monte Carlo simulations of fermionic systems.

Conclusions:

  • This quantum algorithm provides a powerful new tool for studying quantum systems.
  • It has broad applications in condensed matter physics, quantum chemistry, and materials science.
  • Enables efficient preparation of thermal states for simulating complex quantum phenomena and chemical reactions.