Related Experiment Video
Updated: Jun 12, 2025

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
Published on: September 8, 2023
A comprehensive review of quantum machine learning: from NISQ to fault tolerance
Yunfei Wang1,2, Junyu Liu3,4,5,6
1Joint Center for Quantum Information and Computer Science, NIST/University of Maryland, College Park, MD 20742, United States of America.
Abstract:
Quantum machine learning, which involves running machine learning algorithms on quantum devices, has garnered significant attention in both academic and business circles. In this paper, we offer a comprehensive and unbiased review of the various concepts that have emerged in the field of quantum machine learning. This includes techniques used in Noisy Intermediate-Scale Quantum (NISQ) technologies and approaches for algorithms compatible with fault-tolerant quantum computing hardware. Our review covers fundamental concepts, algorithms, and the statistical learning theory pertinent to quantum machine learning.
Related Concept Videos
Multimachine Stability
In analyzing the system, the nodal equations represent the relationship between bus voltages, machine voltages, and machine currents. The nodal equation is given by:
Ampere-Maxwell's Law: Problem-Solving
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...
The Quantum-Mechanical Model of an Atom
NMR Spectrometers: Resolution and Error Correction
BIBO stability of continuous and discrete -time systems
To determine the BIBO stability, the convolution integral is utilized when a bounded continuous-time input is applied to a Linear Time-Invariant (LTI) system....
Ampere's Law: Problem-Solving
Specific steps need to be considered while calculating the symmetric magnetic field distribution...

