76 | Quantum AI: Algorithms and Applications
This course covers various Quantum AI technologies based on Parameterized Quantum Circuits (PQC), one of the quantum technologies that has recently attracted significant attention. To facilitate a clear understanding of PQC-based quantum AI, the course first introduces quantum neural network architectures by integrating fundamental concepts of classical artificial neural networks with quantum computing theory. Building upon this foundation, the course then explores advanced Quantum AI models, including Quantum Reinforcement Learning (QRL), Quantum Federated Learning (QFL), and Quantum Convolutional Neural Networks (QCNNs). Finally, through representative research cases in which Quantum AI algorithms are applied across diverse domains, the course examines the advantages of Quantum AI technologies and their potential for future research directions.
63 | Engineer’s Guide to Machine Learning with Quantum Computers
Quantum computing lecture from the QuCS series.
47 | Quantum Machine Learning on Current Quantum Computers
Quantum computing lecture from the QuCS series.
36 | Hybrid Quantum-Classical Machine Learning with Applications
The development of machine learning (ML) and quantum computing (QC) hardware has generated a lot of interest in creating quantum machine learning (QML) applications.
34 | Optimize Quantum Learning on Near-Term Noisy Quantum Computers
In recent years, there has been a significant breakthrough in the development of superconducting quantum computers, with IBM’s 433-qubit quantum computer being a prime example of the progress made in addressing scalability issues.
21 | Quantum Machine Learning: Theoretical Foundations and Applications on NISQ Devices
Quantum machine learning (QML) is a trailblazing research subject that integrates quantum computing and machine learning.
20 | Learning and Training in Quantum Environments
Quantum computing presents fascinating new opportunities for various applications, including machine learning, simulation, and optimization.
6 | Adaptive Online Learning of Quantum States
Shadow tomography is a fundamental problem in quantum computing, whose goal is to efficiently learn an unknown d-dimensional quantum state using projective measurements.