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

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

642
This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...
642
Expected Value01:15

Expected Value

4.1K
The expected value is known as the "long-term" average or mean. This means that over the long term of experimenting over and over, you would expect this average. The expected average is represented by the symbol μ. It is calculated as follows:
4.1K
Estimation of the Physical Quantities01:05

Estimation of the Physical Quantities

4.7K
On many occasions, physicists, other scientists, and engineers need to make estimates of a particular quantity. These are sometimes referred to as guesstimates, order-of-magnitude approximations, back-of-the-envelope calculations, or Fermi calculations. The physicist Enrico Fermi was famous for his ability to estimate various kinds of data with surprising precision. Estimating does not mean guessing a number or a formula at random. Instead, estimation means using prior experience and sound...
4.7K
Interpreting ¹H NMR Signal Splitting: The (n + 1) Rule01:10

Interpreting ¹H NMR Signal Splitting: The (n + 1) Rule

1.4K
In the AX proton spin system, proton A can sense the two spin states of a coupled proton X, resulting in a doublet NMR signal with two peaks of equal (1:1) intensity. When proton A is coupled to two equivalent protons (AX2 spin system), the spin states of each X can be aligned with or against the external field, creating three possible scenarios. This results in a 1:2:1  triplet signal, where the central peak corresponds to the chemical shift of A and is twice as large or intense as the...
1.4K
Propagation of Uncertainty from Random Error00:59

Propagation of Uncertainty from Random Error

772
An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
772
Equilibrium Conditions for a Particle01:23

Equilibrium Conditions for a Particle

1.3K
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...
1.3K

You might also read

Related Articles

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

Sort by
Same author

Quantum Simulation with Sum-of-Squares Spectral Amplification.

Physical review letters·2026
Same author

Optimization by decoded quantum interferometry.

Nature·2025
Same author

Effects of anesthesia modality on plasma proteomics and biomarkers of inflammation and vascular injury: an exploratory analysis.

Canadian journal of anaesthesia = Journal canadien d'anesthesie·2025
Same author

Shadow hamiltonian simulation.

Nature communications·2025
Same author

Quantum computation of stopping power for inertial fusion target design.

Proceedings of the National Academy of Sciences of the United States of America·2024
Same author

Quantum simulation of exact electron dynamics can be more efficient than classical mean-field methods.

Nature communications·2023
Same journal

Erratum: Bacterial Turbulence at Compressible Fluid Interfaces [Phys. Rev. Lett. 136, 138301 (2026)].

Physical review letters·2026
Same journal

Unveiling Light-Quark Yukawa Flavor Structure via Dihadron Fragmentation at Lepton Colliders.

Physical review letters·2026
Same journal

Adaptable Route to Fast Coherent State Transport via Bang-Bang-Bang Protocols.

Physical review letters·2026
Same journal

Topological Transition and Emergence of Elasticity of Dislocation in Skyrmion Lattice: Beyond Kittel's Magnetic-Polar Analogy.

Physical review letters·2026
Same journal

Pound-Drever-Hall Method for Superconducting-Qubit Readout.

Physical review letters·2026
Same journal

Coupling a ^{73}Ge Nuclear Spin to an Electrostatically Defined Quantum Dot in Silicon.

Physical review letters·2026
See all related articles

Related Experiment Video

Updated: Aug 16, 2025

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

14.6K

Nearly Optimal Quantum Algorithm for Estimating Multiple Expectation Values.

William J Huggins1, Kianna Wan1,2, Jarrod McClean1

  • 1Google Quantum AI, Mountain View, 94043 California, USA.

Physical Review Letters
|December 23, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a new quantum algorithm for estimating multiple expectation values efficiently. It achieves a faster O(sqrt[M]/ϵ) scaling, improving upon existing methods for quantum computing tasks.

More Related Videos

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

9.1K
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

634

Related Experiment Videos

Last Updated: Aug 16, 2025

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

14.6K
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

9.1K
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

634

Area of Science:

  • Quantum Computing
  • Quantum Algorithms
  • Quantum Information Science

Background:

  • Estimating expectation values is crucial for many quantum algorithms.
  • Current methods for a single expectation value scale as O(1/ϵ).
  • Estimating multiple expectation values efficiently presents a significant challenge.

Purpose of the Study:

  • To develop an efficient method for estimating M expectation values simultaneously.
  • To achieve a scaling of O(sqrt[M]/ϵ) for estimating multiple expectation values.
  • To prove the optimality of the proposed scaling in the high-precision regime.

Main Methods:

  • Leveraging Gilyén et al.'s quantum gradient estimation algorithm.
  • Developing a novel approach for simultaneous estimation of M observables.
  • Analyzing the scaling of the algorithm with respect to M and ϵ.

Main Results:

  • Achieved an O(sqrt[M]/ϵ) scaling for estimating M expectation values, up to logarithmic factors.
  • Demonstrated that this scaling is worst-case optimal when state preparation is a black box.
  • Showcased flexibility through generalizations, including dynamic correlation functions.

Conclusions:

  • The proposed quantum algorithm offers a significant speedup for estimating multiple expectation values.
  • The method is robust, working regardless of the commutation properties of the observables.
  • This advancement has broad implications for various quantum algorithms and simulations.