Results 1 to 10 of about 295,942 (135)

Case-Based and Quantum Classification for ERP-Based Brain–Computer Interfaces [PDF]

Brain Sciences, 2023
Low transfer rates are a major bottleneck for brain–computer interfaces based on electroencephalography (EEG). This problem has led to the development of more robust and accurate classifiers.
Grégoire H. Cattan, Alexandre Quemy
doaj   +2 more sources

On Quantum Methods for Machine Learning Problems Part II: Quantum Classification Algorithms

Big Data Mining and Analytics, 2020
This is a review of quantum methods for machine learning problems that consists of two parts. The first part, "quantum tools", presented some of the fundamentals and introduced several quantum tools based on known quantum search algorithms.
Farid Ablayev, Marat Ablayev, Joshua Zhexue Huang, Kamil Khadiev, Nailya Salikhova, Dingming Wu   +5 more
doaj   +3 more sources

Quantum phase classification via partial tomography-based quantum hypothesis testing [PDF]

Scientific Reports
Quantum phase classification is a fundamental problem in quantum many-body physics, traditionally approached using order parameters or quantum machine learning techniques such as quantum convolutional neural networks (QCNNs). However, these methods often
Akira Tanji, Hiroshi Yano, Naoki Yamamoto   +2 more
doaj   +2 more sources

A Multi-Classification Hybrid Quantum Neural Network Using an All-Qubit Multi-Observable Measurement Strategy

Entropy, 2022
Quantum machine learning is a promising application of quantum computing for data classification. However, most of the previous research focused on binary classification, and there are few studies on multi-classification.
Yi Zeng, Hao Wang, Jin He, Qijun Huang, Sheng Chang   +4 more
doaj   +3 more sources

Benchmarking MedMNIST dataset on real quantum hardware [PDF]

Scientific Reports
Quantum machine learning (QML) has emerged as a promising domain to leverage the computational capabilities of quantum systems to solve complex classification tasks.
Gurinder Singh, Hongni Jin, Kenneth M. Merz Jr.   +2 more
doaj   +2 more sources

Multiclass classification using quantum convolutional neural networks with hybrid quantum-classical learning

Frontiers in Physics, 2022
Multiclass classification is of great interest for various applications, for example, it is a common task in computer vision, where one needs to categorize an image into three or more classes.
Denis Bokhan, Denis Bokhan, Alena S. Mastiukova, Alena S. Mastiukova, Aleksey S. Boev, Dmitrii N. Trubnikov, Aleksey K. Fedorov, Aleksey K. Fedorov   +7 more
doaj   +3 more sources

Quantum inspired feature engineering for explainable EEG signal classification [PDF]

Scientific Reports
In this research, our main objective is to extract more informative features by deploying a simple and effective framework. One of the cheapest data-gathering methods from the brain is electroencephalography signal collection.
Fahad A. Alotaibi, Mehmet Said Nur Yagmahan, Khalid A. Alobaid, Mousa Jari, Omer Faruk Goktas, Mehmet Baygin, Sengul Dogan, Turker Tuncer   +7 more
doaj   +2 more sources

Deep learning for classifying quantum emission signals in WS2 monolayers using wavelet transform [PDF]

Scientific Reports
This study aimed to develop and evaluate deep learning approaches for the classification of quantum emission signals from WS2 monolayer nanobubbles across multiple spectral bands, addressing challenges in quantum materials characterization and spectral ...
Hossein Najafzadeh, Zahra Raissi, Shole Golmohammady, Parivash Safari Kaji, Mahdad Esmaeili   +4 more
doaj   +2 more sources

Quantum Machine Learning: A Review and Case Studies

Entropy, 2023
Despite its undeniable success, classical machine learning remains a resource-intensive process. Practical computational efforts for training state-of-the-art models can now only be handled by high speed computer hardware.
Amine Zeguendry, Zahi Jarir, Mohamed Quafafou   +2 more
doaj   +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