Results 21 to 30 of about 137,793 (228)
Quantum machine learning: a classical perspective [PDF]
, 2018 Recently, increased computational power and data availability, as well as algorithmic advances, have led machine learning techniques to impressive results in regression, classification, data-generation and reinforcement learning tasks.
Ben-David S +15 more
core +2 more sources
IEEE Open Journal of Vehicular Technology, 2022 Quantum computing is envisaged as an evolving paradigm for solving computationally complex optimization problems with a large-number factorization and exhaustive search.
Trung Q. Duong +5 more
doaj +1 more source
Supervised Quantum Learning without Measurements [PDF]
, 2017 We propose a quantum machine learning algorithm for efficiently solving a class of problems encoded in quantum controlled unitary operations. The central physical mechanism of the protocol is the iteration of a quantum time-delayed equation that ...
Alvarez-Rodriguez, Unai +4 more
core +5 more sources
Quantum adiabatic machine learning [PDF]
Quantum Information Processing, 2012 21 pages, 9 ...
Kristen L. Pudenz, Daniel A. Lidar
openaire +2 more sources
Discriminating Quantum States with Quantum Machine Learning [PDF]
2021 IEEE International Conference on Quantum Computing and Engineering (QCE), 2021 Quantum machine learning (QML) algorithms have obtained great relevance in the machine learning (ML) field due to the promise of quantum speedups when performing basic linear algebra subroutines (BLAS), a fundamental element in most ML algorithms. By making use of BLAS operations, we propose, implement and analyze a quantum k-means (qk-means) algorithm
David A. Quiroga +2 more
openaire +2 more sources
Progress in Constraining Nuclear Symmetry Energy Using Neutron Star Observables Since GW170817
Universe, 2021 The density dependence of nuclear symmetry energy is among the most uncertain parts of the Equation of State (EOS) of dense neutron-rich nuclear matter.
Bao-An Li +3 more
doaj +1 more source
Distributed Quantum Machine Learning
, 2022 Quantum computers can solve specific complex tasks for which no reasonable-time classical algorithm is known. Quantum computers do however also offer inherent security of data, as measurements destroy quantum states. Using shared entangled states, multiple parties can collaborate and securely compute quantum algorithms.
Niels M. P. Neumann, Robert S. Wezeman
openaire +3 more sources
Machine learning and quantum devices [PDF]
SciPost Physics Lecture Notes, 2021 These brief lecture notes cover the basics of neural networks and deep learning as well as their applications in the quantum domain, for physicists without prior knowledge. In the first part, we describe training using backpropagation, image classification, convolutional networks and autoencoders.
openaire +4 more sources
Energy Reports, 2022 Infrastructure development is expected to support economic growth in Indonesia. Infrastructure development related to logistics and energy is a top priority based on the Indonesia’s development plan.
Muhammad Arif Budiyanto +3 more
doaj +1 more source
Machine Learning for Quantum Metrology [PDF]
11th Italian Quantum Information Science conference (IQIS2018), 2019 Phase estimation represents a significant example to test the application of quantum theory for enhanced measurements of unknown physical parameters. Several recipes have been developed, allowing to define strategies to reach the ultimate bounds in the asymptotic limit of a large number of trials. However, in certain applications it is crucial to reach
Spagnolo, Nicolò +5 more
openaire +2 more sources