Quantum Machine Learning

88 papers for year 2018

Quantum Machine Learning Research Context

This category covers quantum machine learning research, including quantum kernels, variational classifiers, hybrid learning systems, generative models, and QML benchmarks.

Source: All sources

All sources 88 arXiv preprints 71 Crossref 6 Europe PMC 9 DOAJ 2

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Showing 37-48 of 88

Improved Quantum Multicollision-Finding Algorithm

Akinori Hosoyamada, Yu Sasaki, Seiichiro Tani, Keita Xagawa

2018 arXiv arXiv preprint

Improvement of optical image by measurement reduction technique at parametric multiplexing

D. A. Balakin, A. S. Chirkin

2018 arXiv arXiv preprint

Learning Robust and High-Precision Quantum Controls

Re-Bing Wu, Haijin Ding, Daoyi Dong, Xiaoting Wang

2018 arXiv arXiv preprint

Logical structures underlying quantum computing

Federico Holik, Giuseppe Sergioli, Hector Freytes, Angelo Plastino

2018 arXiv arXiv preprint

Machine Learning for Optimal Parameter Prediction in Quantum Key Distribution

Wenyuan Wang, Hoi-Kwong Lo

2018 arXiv arXiv preprint

Measurement-based adaptation protocol with quantum reinforcement learning in a Rigetti quantum computer

J. Olivares-Sánchez, J. Casanova, E. Solano, L. Lamata

2018 arXiv arXiv preprint

Measuring distances in Hilbert space by many-particle interference

Karol Bartkiewicz, Vojtěch Trávníček, Karel Lemr

2018 arXiv arXiv preprint

Modelling non-markovian quantum processes with recurrent neural networks

Leonardo Banchi, Edward Grant, Andrea Rocchetto, Simone Severini

2018 Crossref New Journal of Physics

Multiqubit and multilevel quantum reinforcement learning with quantum technologies.

F A Cárdenas-López, L Lamata, J C Retamal, E Solano

2018 DOAJ PLoS ONE

Neural network state estimation for full quantum state tomography

Qian Xu, Shuqi Xu

2018 arXiv arXiv preprint

Nonequilibrium fluctuations of a driven quantum heat engine via machine learning

Sajal Kumar Giri, Himangshu Prabal Goswami

2018 arXiv arXiv preprint

On quantum illumination, quantum reading, and the capacity of quantum computation

Stefano Pirandola

2018 arXiv arXiv preprint

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