Results 61 to 70 of about 8,389,880 (297)

Quantum Based Pseudo-Labelling for Hyperspectral Imagery: A Simple and Efficient Semi-Supervised Learning Method for Machine Learning Classifiers

Remote Sensing, 2022
A quantum machine is a human-made device whose collective motion follows the laws of quantum mechanics. Quantum machine learning (QML) is machine learning for quantum computers.
Riyaaz Uddien Shaik, Aiswarya Unni, Weiping Zeng   +2 more
doaj   +1 more source

Multimodal Optoelectronic Synapses for Neuromorphic Intelligence: Coupling Light, Electrical, Thermal, and Beyond

Advanced Functional Materials, EarlyView.
This review revisits optoelectronic neuromorphic devices that extend from unimodal sensing toward multimodal perception. Five types of stimuli‐to‐output information flow that are classified based on the device roles within the system and applications are summarized and discussed.
Jihyun Shin, Junhyung Cho, Wangmyung Choi, Jaehyun Hur, Jae Kyeong Jeong, Ulrike Kraft, Hocheon Yoo   +6 more
wiley   +1 more source

NetKet: A machine learning toolkit for many-body quantum systems

SoftwareX, 2019
We introduce NetKet, a comprehensive open source framework for the study of many-body quantum systems using machine learning techniques. The framework is built around a general and flexible implementation of neural-network quantum states, which are used ...
Giuseppe Carleo, Kenny Choo, Damian Hofmann, James E.T. Smith, Tom Westerhout, Fabien Alet, Emily J. Davis, Stavros Efthymiou, Ivan Glasser, Sheng-Hsuan Lin, Marta Mauri, Guglielmo Mazzola, Christian B. Mendl, Evert van Nieuwenburg, Ossian O’Reilly, Hugo Théveniaut, Giacomo Torlai, Filippo Vicentini, Alexander Wietek   +18 more
doaj   +1 more source

Kernel methods in Quantum Machine Learning

, 2019
Quantum Machine Learning has established itself as one of the most promising applications of quantum computers and Noisy Intermediate Scale Quantum (NISQ) devices.
Alessandra Di Pierro
core   +1 more source

Emerging Post‐CMOS Hardware Neurons for Brain‐Inspired Computing: Devices, Circuits, and System Integration

Advanced Functional Materials, EarlyView.
The physical realization of artificial neurons is a critical challenge for energy‐efficient neuromorphic computing. This review presents a comprehensive analysis of the evolution of artificial neuron implementations from conventional CMOS to emerging post‐CMOS technologies.
Kannan Udaya Mohanan, Hocheon Yoo, Benoît H. Lessard, Ioannis Kymissis, Chang‐Hyun Kim   +4 more
wiley   +1 more source

Variational Quantum Circuits for Deep Reinforcement Learning

IEEE Access, 2020
The state-of-the-art machine learning approaches are based on classical von Neumann computing architectures and have been widely used in many industrial and academic domains.
Samuel Yen-Chi Chen, Chao-Han Huck Yang, Jun Qi, Pin-Yu Chen, Xiaoli Ma, Hsi-Sheng Goan   +5 more
doaj   +1 more source

Quantum Machine Learning for Enhanced Cybersecurity: Proposing a Hypothetical Framework for Next-Generation Security Solutions [PDF]

The rapid evolution of cyber threats has rendered conventional security approaches inadequate for managing increasingly sophisticated risks. This study introduces a Quantum Machine Learning Cybersecurity Framework that leverages quantum computing and ...
Amin, Al, Mahmud, Shakik, Hossain, Forhad, Hasan, Kamrul   +3 more
core   +1 more source

Quantum Machine Learning Playground

IEEE Computer Graphics and Applications
Accepted to IEEE Computer Graphics and Applications.
Pascal Debus, Sebastian Issel, Kilian Tscharke   +2 more
openaire   +4 more sources

Reservoir‐Driven Neuromorphic Computing Based on Composite Rare‐Earth/Transition Metal Oxide Memristor

Advanced Functional Materials, EarlyView.
A defect‐engineered Ag/Gd2O3:Nb2O5/Pt rare earth composite oxide memristor enables stable multilevel reservoir states through pulse driven conductance modulation. Experimentally measured device responses are incorporated into a device aware reservoir computing framework for CIFAR‐100 image classification, highlighting the potential of rare earth ...
Hammad Ghazanfar, Haider Abbas, Muhammad Rabeel, Honggyun Kim, Ghulam Dastgeer, Muhammad Nasim, Furqan Zahoor, Yawar Abbas, Waqas Ahmad, Deok‐kee Kim   +9 more
wiley   +1 more source

Indoor–Outdoor Detection in Mobile Networks Using Quantum Machine Learning Approaches

Computers, 2021
Communication networks are managed more and more by using artificial intelligence. Anomaly detection, network monitoring and user behaviour are areas where machine learning offers advantages over more traditional methods.
Frank Phillipson, Robert S. Wezeman, Irina Chiscop   +2 more
doaj   +1 more source