Results 21 to 30 of about 23,653 (255)
Federated Quantum Machine Learning. [PDF]
Entropy (Basel), 2021 Distributed training across several quantum computers could significantly improve the training time and if we could share the learned model, not the data, it could potentially improve the data privacy as the training would happen where the data is located.
Chen SY, Yoo S.
europepmc +5 more sources
Experimental Evaluation of Quantum Machine Learning Algorithms
IEEE Access, 2023 Machine learning and quantum computing are both areas with considerable progress in recent years. The combination of these disciplines holds great promise for both research and practical applications.
Ricardo Daniel Monteiro Simoes +5 more
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Quantum adiabatic machine learning [PDF]
Quantum Information Processing, 2012 21 pages, 9 ...
Kristen L. Pudenz, Daniel A. Lidar
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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
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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
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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
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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.
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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
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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
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Quantum Fair Machine Learning [PDF]
Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society, 2021 In this paper, we inaugurate the field of quantum fair machine learning. We undertake a comparative analysis of differences and similarities between classical and quantum fair machine learning algorithms, specifying how the unique features of quantum computation alter measures, metrics and remediation strategies when quantum algorithms are subject to ...
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