Machine learning algorithms in quantum computing: A survey
SB Ramezani, A Sommers… - … joint conference on …, 2020 - ieeexplore.ieee.org
Machine Learning (ML) aims at designing models that learn from previous experience,
without being explicitly formulated. Applications of machine learning are inexhaustible …
without being explicitly formulated. Applications of machine learning are inexhaustible …
Advances in quantum machine learning
Here we discuss advances in the field of quantum machine learning. The following
document offers a hybrid discussion; both reviewing the field as it is currently, and …
document offers a hybrid discussion; both reviewing the field as it is currently, and …
Configurable sublinear circuits for quantum state preparation
The theory of quantum algorithms promises unprecedented benefits of harnessing the laws
of quantum mechanics for solving certain computational problems. A prerequisite for …
of quantum mechanics for solving certain computational problems. A prerequisite for …
Quantum neural networks: Current status and prospects for development
MV Altaisky, NE Kaputkina, VA Krylov - Physics of Particles and Nuclei, 2014 - Springer
The idea of quantum artificial neural networks, first formulated in [34], unites the artificial
neural network concept with the quantum computation paradigm. Quantum artificial neural …
neural network concept with the quantum computation paradigm. Quantum artificial neural …
[PDF][PDF] 量子机器学习算法综述
黄一鸣， 雷航， 李晓瑜 - 计算机学报, 2018 - cjc.ict.ac.cn
摘要机器学习在过去十几年里不断发展, 并对其他领域产生了深远的影响. 近几年,
研究人员发现结合量子计算特性的新型机器学习算法可实现对传统算法的加速 …
研究人员发现结合量子计算特性的新型机器学习算法可实现对传统算法的加速 …
Superposition learning-based model for prediction of E. coli in groundwater using physico-chemical water quality parameters
The prediction of waterborne bacteria is crucial to prevent health risks. Therefore, there is a
need to study the quality of groundwater by predicting the presence of E. coli. The …
need to study the quality of groundwater by predicting the presence of E. coli. The …
Classical and superposed learning for quantum weightless neural networks
A supervised learning algorithm for quantum neural networks (QNN) based on a novel
quantum neuron node implemented as a very simple quantum circuit is proposed and …
quantum neuron node implemented as a very simple quantum circuit is proposed and …
Weightless neural network parameters and architecture selection in a quantum computer
Training artificial neural networks requires a tedious empirical evaluation to determine a
suitable neural network architecture. To avoid this empirical process several techniques …
suitable neural network architecture. To avoid this empirical process several techniques …
[PDF][PDF] Advances in weightless neural systems
FMG França, M De Gregorio, PMV Lima… - … on Artificial Neural …, 2014 - academia.edu
Random Access Memory (RAM) nodes can play the role of artificial neurons that are
addressed by Boolean inputs and produce Boolean outputs. The weightless neural network …
addressed by Boolean inputs and produce Boolean outputs. The weightless neural network …
Parametric probabilistic quantum memory
RS Sousa, PGM dos Santos, TML Veras… - Neurocomputing, 2020 - Elsevier
Abstract Probabilistic Quantum Memory (PQM) is a data structure that computes the distance
from a binary input to all binary patterns stored in superposition on the memory. This data …
from a binary input to all binary patterns stored in superposition on the memory. This data …