Results 51 to 60 of about 2,977,466 (210)

Implementing Magnetic Resonance Imaging Brain Disorder Classification via AlexNet–Quantum Learning

Mathematics, 2023
The classical neural network has provided remarkable results to diagnose neurological disorders against neuroimaging data. However, in terms of efficient and accurate classification, some standpoints need to be improved by utilizing high-speed computing ...
Naif Alsharabi, Tayyaba Shahwar, Ateeq Ur Rehman, Yasser Alharbi   +3 more
doaj   +1 more source

Quantum discriminator for binary classification

Scientific Reports
AbstractQuantum computers have the unique ability to operate relatively quickly in high-dimensional spaces—this is sought to give them a competitive advantage over classical computers. In this work, we propose a novel quantum machine learning model called the Quantum Discriminator, which leverages the ability of quantum computers to operate in the high-
Date, Prasanna, Smith, Wyatt
openaire   +4 more sources

Rapid Classification of Quantum Sources Enabled by Machine Learning

, 2020
Deterministic nanoassembly may enable unique integrated on-chip quantum photonic devices. Such integration requires a careful large-scale selection of nanoscale building blocks such as solid-state single-photon emitters by means of optical ...
Theodor Isacsson, Vladimir M. Shalaev, Bogdanov, S. I., Boltasseva, A., Kildishev, A. V., Shalaev, V. M., Zhaxylyk A. Kudyshev, Simeon I. Bogdanov, Alexander V. Kildishev, Alexandra Boltasseva, Kudyshev, Z. A., Isacsson, Theodor,   +11 more
core   +1 more source

A Comparative Study of Quantum Feature Maps and Quantum Classifiers for Heart Disease Prediction

AI
This research introduces a quantum machine learning (QML) approach for predicting heart disease (HD). The method combines preprocessing of data with quantum feature map (QFM) and quantum classification techniques.
Muhammad Minoar Hossain, Md. Hasibul Hassan Himal, Arslan Munir   +2 more
doaj   +1 more source

Application of quantum machine learning using quantum kernel algorithms on multiclass neuron M-type classification

Scientific Reports, 2023
The functional characterization of different neuronal types has been a longstanding and crucial challenge. With the advent of physical quantum computers, it has become possible to apply quantum machine learning algorithms to translate theoretical ...
Xavier Vasques, Hanhee Paik, Laura Cif
doaj   +1 more source

Quantum K-Nearest Neighbors: Utilizing QRAM and SWAP-Test Techniques for Enhanced Performance

Mathematics
This work introduces a quantum K-Nearest Neighbor (K-NN) classifier algorithm. The algorithm utilizes angle encoding through a Quantum Random Access Memory (QRAM) using n number of qubit addresses with O(log(n)) space complexity. It incorporates Grover’s
Alberto Maldonado-Romo, J. Yaljá Montiel-Pérez, Victor Onofre, Javier Maldonado-Romo , Juan Humberto Sossa-Azuela    +4 more
doaj   +1 more source

Multiclass Classification of Metrologically Resourceful Tripartite Quantum States with Deep Neural Networks

Sensors, 2022
Quantum entanglement is a unique phenomenon of quantum mechanics, which has no classical counterpart and gives quantum systems their advantage in computing, communication, sensing, and metrology.
Syed Muhammad Abuzar Rizvi, Naema Asif, Muhammad Shohibul Ulum, Trung Q. Duong, Hyundong Shin   +4 more
doaj   +1 more source

Quantum classification

CoRR, 2008
Quantum classification is defined as the task of predicting the associated class of an unknown quantum state drawn from an ensemble of pure states given a finite number of copies of this state. By recasting the state discrimination problem within the framework of Machine Learning (ML), we can use the notion of learning reduction coming from classical ...
openaire   +2 more sources

Quantum-enhanced data classification with a variational entangled sensor network

, 2020
Variational quantum circuits (VQCs) built upon noisy intermediate-scale quantum (NISQ) hardware, in conjunction with classical processing, constitute a promising architecture for quantum simulations, classical optimization, and machine learning. However,
Wei Li (7081), Quntao Zhuang (13019790), Zheshen Zhang (11470132), Yi Xia (294184)   +3 more
core   +1 more source

CUDA Quantum

<p>The 0.7.0 release adds support for using NVIDIA Quantum Cloud, giving you access to our most powerful GPU-accelerated simulators even if you don't have an NVIDIA GPU. With 0.7.0, we have furthermore greatly increased expressiveness of the Python
The CUDA Quantum development team
core   +2 more sources