Results 161 to 170 of about 8,042,432 (193)

Publisher Correction: Overcoming the coherence time barrier in quantum machine learning on temporal data. [PDF]

Nat Commun
Hu F, Khan SA, Bronn NT, Angelatos G, Rowlands GE, Ribeill GJ, Türeci HE.   +6 more
europepmc   +1 more source

Understanding quantum machine learning also requires rethinking generalization. [PDF]

Nat Commun
Gil-Fuster E, Eisert J, Bravo-Prieto C.
europepmc   +1 more source

SPA^HM(a,b): Encoding the Density Information from Guess Hamiltonian in Quantum Machine Learning Representations. [PDF]

J Chem Theory Comput
Briling KR, Calvino Alonso Y, Fabrizio A, Corminboeuf C.   +3 more
europepmc   +1 more source

Quantum machine learning [PDF]

Nature, 2017
Fuelled by increasing computer power and algorithmic advances, machine learning techniques have become powerful tools for finding patterns in data. Since quantum systems produce counter-intuitive patterns believed not to be efficiently produced by classical systems, it is reasonable to postulate that quantum computers may outperform classical computers
Nicola Pancotti, Jacob Biamonte, Peter Wittek   +2 more
exaly   +6 more sources

Quantum enhanced machine learning: An overview [PDF]

CEUR Workshop Proceedings, Vol-2832: Proceedings of the 3rd Workshop for Young Scientists in Computer Science & Software Engineering (CS&SE@SW 2020), 2021
Machine learning is now widely used almost everywhere, primarily for forecasting. The main idea of the work is to identify the possibility of achieving a quantum advantage when solving machine learning problems on a quantum computer.
Zahorodko, Pavlo V., Modlo, Ye.O., Kalinichenko, Olga O., Selivanova, T.V., Semerikov, Serhiy O.   +4 more
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Quantum Driven Machine Learning

International Journal of Theoretical Physics, 2020
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Shivani Saini, PK Khosla, Manjit Kaur, Gurmohan Singh   +3 more
openaire   +1 more source

Generative Quantum Machine Learning

2021
The goal of generative machine learning is to model the probability distribution underlying a given data set. This probability distribution helps to characterize the generation process of the data samples. While classical generative machine learning is solely based on classical resources, generative quantum machine learning can also employ quantum ...
openaire   +1 more source

Quantum Algebraic Machine Learning

2020 IEEE 10th International Conference on Intelligent Systems (IS), 2020
Quantum information processing is a rapidly growing industry with promising results in the near future. Its implementtation has serious impact on almost any areas of our lives: cyber-security, communication, AI, etc. Any computations can be considered as quantum ones and in this context classical computations are a subset of quantum computations.
openaire   +2 more sources

Machine Learning in a Quantum World

2006
Quantum Information Processing (QIP) performs wonders in a world that obeys the laws of quantum mechanics, whereas Machine Learning (ML) is generally assumed to be done in a classical world. We initiate an investigation of the encounter of ML with QIP by defining and studying novel learning tasks that correspond to Machine Learning in a world in which ...
Esma Aïmeur, Gilles Brassard, Sébastien Gambs   +2 more
openaire   +1 more source