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

Systematic Error: Methodological and Sampling Errors01:15

Systematic Error: Methodological and Sampling Errors

1.5K
In the case of systematic errors, the sources can be identified, and the errors can be subsequently minimized by addressing these sources. According to the source, systematic errors can be divided into sampling, instrumental, methodological, and personal errors.
Sampling errors originate from improper sampling methods or the wrong sample population. These errors can be minimized by refining the sampling strategy. Defective instruments or faulty calibrations are the sources of instrumental...
1.5K
Contaminants and Errors01:16

Contaminants and Errors

94
Effective sample preparation is crucial for accurate and reliable laboratory analysis. During this process, two significant sources of error can arise: concentration bias from improper sample splitting and contamination caused by methods used to reduce particle size, such as grinding or homogenization. Identifying and minimizing these potential errors is crucial to ensuring the validity of the analysis.
Another key consideration is determining the appropriate number of samples required to...
94
Sampling Theorem01:15

Sampling Theorem

345
In signal processing, the analysis of continuous-time signals, denoted as x(t), often involves sampling techniques to convert these signals into discrete-time signals. This process is essential for digital representation and manipulation. A critical component in sampling is the train of impulses, characterized by the sampling interval and the sampling frequency. The relationship between these parameters and the original signal's properties dictates the success of the sampling process.
345
Upsampling01:22

Upsampling

238
Managing signal sampling rates is essential in digital signal processing to maintain signal integrity. A decimated signal, characterized by a reduced frequency range due to its lower sampling rate, can be upsampled by inserting zeros between each sample. This upsampling process expands the original spectrum and introduces repeated spectral replicas at intervals dictated by the new Nyquist frequency. To refine this zero-inserted sequence, it is passed through a lowpass filter with a cutoff...
238
Propagation of Uncertainty from Random Error00:59

Propagation of Uncertainty from Random Error

698
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...
698
Uncertainty in Measurement: Accuracy and Precision03:37

Uncertainty in Measurement: Accuracy and Precision

73.7K
Scientists typically make repeated measurements of a quantity to ensure the quality of their findings and to evaluate both the precision and the accuracy of their results. Measurements are said to be precise if they yield very similar results when repeated in the same manner. A measurement is considered accurate if it yields a result that is very close to the true or the accepted value. Precise values agree with each other; accurate values agree with a true value. 
73.7K

You might also read

Related Articles

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

Sort by
Same author

Universal work extraction in quantum thermodynamics.

Nature communications·2026
Same author

Improving the balance of trade-offs in multi-objective optimization with quantum computing.

Nature computational science·2025
Same author

Energetic Advantages for Quantum Agents in Online Execution of Complex Strategies.

Physical review letters·2025
Same author

Symmetry Induced Enhancement in Finite-Time Thermodynamic Trade-Off Relations.

Physical review letters·2025
Same author

Black Box Work Extraction and Composite Hypothesis Testing.

Physical review letters·2025
Same author

Variational Quantum Circuit Decoupling.

Physical review letters·2024
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: Jul 8, 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

Universal Sampling Lower Bounds for Quantum Error Mitigation.

Ryuji Takagi1,2, Hiroyasu Tajima3,4, Mile Gu2,5,6

  • 1Department of Basic Science, The University of Tokyo, Tokyo 153-8902, Japan.

Physical Review Letters
|December 10, 2023
PubMed
Summary
This summary is machine-generated.

Quantum error mitigation faces fundamental sampling cost limitations. Achieving accurate results with quantum error mitigation requires exponentially more runs as circuit depth increases, hindering scalable quantum devices.

More Related Videos

Measurement of Quantum Interference in a Silicon Ring Resonator Photon Source
12:19

Measurement of Quantum Interference in a Silicon Ring Resonator Photon Source

Published on: April 4, 2017

8.4K
A Photonic System for Generating Unconditional Polarization-Entangled Photons Based on Multiple Quantum Interference
00:07

A Photonic System for Generating Unconditional Polarization-Entangled Photons Based on Multiple Quantum Interference

Published on: September 5, 2019

8.5K

Related Experiment Videos

Last Updated: Jul 8, 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
Measurement of Quantum Interference in a Silicon Ring Resonator Photon Source
12:19

Measurement of Quantum Interference in a Silicon Ring Resonator Photon Source

Published on: April 4, 2017

8.4K
A Photonic System for Generating Unconditional Polarization-Entangled Photons Based on Multiple Quantum Interference
00:07

A Photonic System for Generating Unconditional Polarization-Entangled Photons Based on Multiple Quantum Interference

Published on: September 5, 2019

8.5K

Area of Science:

  • Quantum Computing
  • Quantum Information Science

Background:

  • Noise significantly impacts intermediate-scale quantum devices.
  • Quantum error mitigation is crucial for reliable quantum computation.
  • The sampling cost of error mitigation remains poorly understood.

Purpose of the Study:

  • To establish universal lower bounds on the sampling cost for quantum error mitigation.
  • To understand the fundamental limitations and feasibility of quantum error mitigation protocols.
  • To analyze the scalability of noisy quantum devices.

Main Methods:

  • Derivation of universal lower bounds on sampling cost.
  • Analysis of general quantum error mitigation protocols, including nonlinear ones.
  • Characterization of sampling cost scaling with circuit depth and noise models.

Main Results:

  • Established universal lower bounds on sampling cost for quantum error mitigation.
  • Demonstrated that sampling cost often scales exponentially with circuit depth.
  • Identified fundamental obstacles to the scalability of noisy quantum devices.

Conclusions:

  • Quantum error mitigation protocols have inherent and often exponential sampling cost limitations.
  • The scalability of useful noisy quantum devices is fundamentally challenged by error mitigation costs.
  • Further research is needed to overcome these sampling cost barriers.