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

First-Order Circuits01:15

First-Order Circuits

1.4K
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...
1.4K
Second-Order Circuits01:17

Second-Order Circuits

1.3K
Integrating two fundamental energy storage elements in electrical circuits results in second-order circuits, encompassing RLC circuits and circuits with dual capacitors or inductors (RC and RL circuits). Second-order circuits are identified by second-order differential equations that link input and output signals.
Input signals typically originate from voltage or current sources, with the output often representing voltage across the capacitor and/or current through the inductor. For example, in...
1.3K
Classification of Systems-II01:31

Classification of Systems-II

139
Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
139
Ampere-Maxwell's Law: Problem-Solving01:17

Ampere-Maxwell's Law: Problem-Solving

613
A parallel-plate capacitor with capacitance C, whose plates have area A and separation distance d, is connected to a resistor R and a battery of voltage V. The current starts to flow at t = 0. What is the displacement current between the capacitor plates at time t? From the properties of the capacitor, what is the corresponding real current?
To solve the problem, we can use the equations from the analysis of an RC circuit and Maxwell's version of Ampère's law.
For the first part of...
613
Classification of Systems-I01:26

Classification of Systems-I

179
Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
179
Network Function of a Circuit01:25

Network Function of a Circuit

280
Frequency response analysis in electrical circuits provides vital insights into a circuit's behavior as the frequency of the input signal changes. The transfer function, a mathematical tool, is instrumental in understanding this behavior. It defines the relationship between phasor output and input and comes in four types: voltage gain, current gain, transfer impedance, and transfer admittance. The critical components of the transfer function are the poles and zeros.
280

You might also read

Related Articles

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

Sort by
Same author

High-Density CRISPR/Cas12a-Mediated Multiplex Genome Editing Reveals Genome Instability in Allotetraploid Cotton.

Genes·2026
Same author

Enrichment-Free Detection of Trace Exosomes Enabled by Liquid Crystal Optics.

ACS sensors·2026
Same author

Recurrent quantum embedding neural network and its application in vulnerability detection.

Scientific reports·2024
Same author

A general quantum algorithm for numerical integration.

Scientific reports·2024
Same author

Fast reconstruction algorithm based on HMC sampling.

Scientific reports·2023
Same author

EGFR-targeted hybrid lipid nanoparticles for chemo-photothermal therapy against colorectal cancer cells.

Chemistry and physics of lipids·2023

Related Experiment Video

Updated: Jun 21, 2025

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

9.6K

Scalable parameterized quantum circuits classifier.

Xiaodong Ding1, Zhihui Song1, Jinchen Xu1

  • 1Laboratory for Advanced Computing and Intelligence Engineering, Zhengzhou, 450001, China.

Scientific Reports
|July 10, 2024
PubMed
Summary
This summary is machine-generated.

We introduce a scalable parameterized quantum circuits classifier (SPQCC) that significantly improves multi-category classification accuracy and scalability. This quantum machine learning model achieves state-of-the-art results on the MNIST dataset.

More Related Videos

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

531
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

9.0K

Related Experiment Videos

Last Updated: Jun 21, 2025

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

9.6K
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

531
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

9.0K

Area of Science:

  • Quantum Computing
  • Machine Learning
  • Artificial Intelligence

Background:

  • Parameterized quantum circuits (PQC) show limitations in accuracy and scalability for multi-category classification tasks.
  • Existing quantum machine learning models struggle to efficiently handle complex classification problems.

Purpose of the Study:

  • To propose a novel scalable parameterized quantum circuits classifier (SPQCC) that enhances classification accuracy and model scalability.
  • To address the performance limitations of generalized PQC models in multi-category classification.

Main Methods:

  • Developed a scalable parameterized quantum circuits classifier (SPQCC) utilizing per-channel PQC.
  • Combined measurements as output for trainable parameters, minimizing cross-entropy loss for fast convergence.
  • Employed parallel execution of identical PQCs on multiple quantum machines to simplify design and improve scalability.

Main Results:

  • SPQCC demonstrated significantly higher classification accuracy compared to other quantum classification algorithms on the MNIST dataset.
  • Achieved state-of-the-art simulation results, matching or exceeding classical classifiers with numerous trainable parameters.
  • The proposed classifier exhibited excellent scalability and robust classification performance.

Conclusions:

  • SPQCC offers a significant advancement in quantum machine learning for classification tasks.
  • The model overcomes the limitations of traditional PQCs, providing a scalable and accurate solution.
  • This approach paves the way for more powerful quantum classifiers in complex machine learning applications.