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

Quantum Numbers02:43

Quantum Numbers

44.8K
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.
44.8K
Block Diagram Reduction01:22

Block Diagram Reduction

322
The process of deriving the transfer function of a control system often involves reducing its block diagram to a single block. This simplification can be achieved through a series of strategic operations, including relocating branch points and comparators. These operations preserve the overall function of the system while allowing for easier manipulation and combination of blocks.
The first step in this process is the identification and relocation of a branch point. A branch point, where a...
322
Bewley Lattice Diagram01:12

Bewley Lattice Diagram

932
The Bewley lattice diagram, developed by L. V. Bewley, effectively organizes the reflections occurring during transmission-line transients. It visually represents how voltage waves propagate and reflect within a transmission line, making it easier to understand the complex interactions that occur.
932
pV-Diagrams01:18

pV-Diagrams

4.6K
The pV diagram, which is a graph of pressure versus volume of the gas under study, is helpful in describing certain aspects of the substance. When the substance behaves like an ideal gas, the ideal gas equation describes the relationship between its pressure and volume. On a pV diagram, it is common to plot an isotherm, which is a curve showing p as a function of V with the number of molecules and the temperature fixed. Then, for an ideal gas, the product of the pressure of the gas and its...
4.6K
First-Order Circuits01:15

First-Order Circuits

2.3K
First-order electrical circuits, which comprise resistors and a single energy storage element - either a capacitor or an inductor, are fundamental to many electronic systems. These circuits are governed by a first-order differential equation that describes the relationship between input and output signals.
One common example of a first-order circuit is the RC (resistor-capacitor) circuit. These circuits are used in relaxation oscillators such as neon lamp oscillator circuits. When voltage is...
2.3K
Elements of Block Diagrams01:25

Elements of Block Diagrams

411
Block diagrams serve as a visual representation of the input-output relationships within a system. An illustrative example is a heating system, where the set temperature activates the furnace to warm the room to the desired level. Block diagrams are versatile, modeling linear systems through Laplace transform variables and nonlinear systems using time domain variables.
A block diagram typically includes essential elements such as comparators, blocks, and feedback loops. Each of these elements...
411

You might also read

Related Articles

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

Sort by
Same author

A simulation-based dataset for anomaly detection in hydrogen blend transport networks.

Data in brief·2026
Same author

Implementation and Testing of V2I Communication Strategies for Emergency Vehicle Priority and Pedestrian Safety in Urban Environments.

Sensors (Basel, Switzerland)·2025
Same author

Retrieving space-dependent polarization transformations via near-optimal quantum process tomography.

Optics express·2023
Same author

An Internet of Things and Fuzzy Markup Language Based Approach to Prevent the Risk of Falling Object Accidents in the Execution Phase of Construction Projects.

Sensors (Basel, Switzerland)·2021
Same author

A dataset of EEG signals from a single-channel SSVEP-based brain computer interface.

Data in brief·2021
Same author

Bloodstain pattern analysis as optimisation problem.

Forensic science international·2016
Same journal

A harmonized fast-fashion garment-variant dataset for textile circularity and sustainability assessment.

Data in brief·2026
Same journal

Terahertz reflectivity dataset: Reading text on both sides of the page.

Data in brief·2026
Same journal

High-quality draft genome sequence data of <i>Levilactobacillus brevis</i> 3LB isolated from fermented milk koumiss.

Data in brief·2026
Same journal

Interview dataset: Encouraging the development of industrial symbiosis networks in Slovenia - transition to the circular economy.

Data in brief·2026
Same journal

Timeseries of multispectral and radar data and vegetation indices from Sentinel-1, Sentinel-2 and Landsat-8 at field scale.

Data in brief·2026
Same journal

BACI-VI-Bench: A dataset of variational inequality benchmark instances for multi-agent trade-network equilibrium.

Data in brief·2026
See all related articles

Related Experiment Video

Updated: Oct 12, 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

731

A dataset for quantum circuit mapping.

Giovanni Acampora1,2, Roberto Schiattarella1, Alfredo Troiano3

  • 1Department of Physics "Ettore Pancini", University of Naples Federico II, Complesso di Monte Sant'Angelo, Via Cintia 21, Napoli 80126, Italy.

Data in Brief
|November 22, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces novel datasets for quantum circuit mapping, enabling machine learning models to efficiently map quantum circuits onto hardware. This accelerates quantum algorithm development for future large-scale quantum computers.

Keywords:
Machine learning for quantum computingQuantum circuit mappingQuantum computing

More Related Videos

Author Spotlight: Exploring Cellular Processes by Modeling Ligands in Cryo-EM Maps
09:30

Author Spotlight: Exploring Cellular Processes by Modeling Ligands in Cryo-EM Maps

Published on: July 19, 2024

1.6K
Scalable Quantum Integrated Circuits on Superconducting Two-Dimensional Electron Gas Platform
05:39

Scalable Quantum Integrated Circuits on Superconducting Two-Dimensional Electron Gas Platform

Published on: August 2, 2019

9.8K

Related Experiment Videos

Last Updated: Oct 12, 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

731
Author Spotlight: Exploring Cellular Processes by Modeling Ligands in Cryo-EM Maps
09:30

Author Spotlight: Exploring Cellular Processes by Modeling Ligands in Cryo-EM Maps

Published on: July 19, 2024

1.6K
Scalable Quantum Integrated Circuits on Superconducting Two-Dimensional Electron Gas Platform
05:39

Scalable Quantum Integrated Circuits on Superconducting Two-Dimensional Electron Gas Platform

Published on: August 2, 2019

9.8K

Area of Science:

  • Quantum Computing
  • Machine Learning
  • Computational Science

Background:

  • Quantum computing offers advantages over classical computing but faces challenges in mapping algorithms to physical processors.
  • Current deterministic mapping algorithms are time-consuming, hindering scalability for next-generation quantum computers with thousands of qubits.

Purpose of the Study:

  • To address the quantum circuit mapping problem by proposing the first datasets suitable for a classification task.
  • To facilitate the development of machine learning models for efficient quantum circuit mapping.

Main Methods:

  • Creation of datasets comprising random quantum circuits mapped to specific IBM quantum processors.
  • Each dataset instance includes device calibration data and quantum circuit features.
  • Instances are labeled with optimal mappings derived from deterministic algorithms.

Main Results:

  • The proposed datasets enable the training of machine learning models for quantum circuit mapping.
  • These models can achieve mapping quality comparable to deterministic methods.

Conclusions:

  • The developed datasets significantly reduce the time required for quantum circuit mapping.
  • This research paves the way for more efficient quantum algorithm design and deployment on larger quantum systems.