Collection
Quantum Techniques in Machine Learning 2021
- Submission status
- Closed
This topical collection will include extended versions of the best original results on the development of quantum techniques in machine learning presented at the 5th International Conference on Quantum Techniques in Machine Learning,
QTML 2021, that was held online on 8-12 November 2021, and organised by Minh Ha Quang (RIKEN Center for Advanced Intelligence Project, Tokyo,
Japan).
Editors
-
Alessandra Di Piero
University of Verona, Italy
-
Minh Ha Quang
RIKEN Center for Advanced Intelligence Project, Tokyo, Japan
-
Nana Liu ,
Nana Liu
Shanghai Jiao Tong University, China
-
Franco Nori
RIKEN Cluster for Pioneering Research, Hirosawa, Japan
-
Birgitta Whaley
University of California, Berkeley, USA
Articles (6 in this collection)
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-
Pairwise classification using quantum support vector machine with Kronecker kernel
Authors
- Taisei Nohara
- Satoshi Oyama
- Itsuki Noda
- Content type: Research Article
- Open Access
- Published: 15 August 2022
- Article: 22
-
Deep tensor networks with matrix product operators
Authors
- Bojan Žunkovič
- Content type: Research Article
- Published: 01 August 2022
- Article: 21
-
Optimisation-free density estimation and classification with quantum circuits
Authors
- Vladimir Vargas-Calderón
- Fabio A. González
- Herbert Vinck-Posada
- Content type: Research Article
- Published: 27 June 2022
- Article: 16
-
A hybrid quantum image edge detector for the NISQ era
Authors (first, second and last of 4)
- Alexander Geng
- Ali Moghiseh
- Katja Schladitz
- Content type: Research Article
- Open Access
- Published: 23 June 2022
- Article: 15
-
Learning classical readout quantum PUFs based on single-qubit gates
Authors
- Niklas Pirnay
- Anna Pappa
- Jean-Pierre Seifert
- Content type: Research Article
- Open Access
- Published: 22 June 2022
- Article: 14