Quantum Techniques on Machine Learning

Acronym: 

QTML 2019

Dates: 

Saturday, October 19, 2019

Registration deadline: 

Saturday, September 14, 2019

Submission deadline: 

Sunday, July 14, 2019

Location: 

KAIST
291 Daehagro, Yuseong
Daejeon
South Korea
KR

QTML 2019 Call for submissions

QTML 2019 is the 3rd in a series of the conference that aims to bring experts from quantum information science and machine learning to discuss the latest progress at the frontier of quantum machine learning. We invite technical presentations for full papers (30 min), short paper (20 min), and posters, on outstanding recent researches in all aspects of quantum machine learning.
Homepage: www.quantummachinelearning.org/qtml2019.html
Example topics include, but are not limited to
· Quantum algorithms for machine learning tasks
· Learning with hybrid quantum-classical methods
· Tensor methods and (deep) learning
· Data encoding and processing in quantum systems
· Quantum learning theory
· Fuzzy logic for quantum machine learning
· Machine learning to design and analyze experiments in quantum information processing

Submission instructions

All submissions for presentations and posters must be made electronically through the online submission system (EasyChair: QTML2019). All submissions should be in the PDF format.

Deadlines
· Extended abstracts for short and full papers: July 15, 2019 at 23:59 (AoE)
· Extended abstracts for poster: August 1, 2019, at 23:59 (AoE)
· Notification of acceptance: August 15, 2019

Extended abstracts for short papers and posters
· A non-technical, clear, and insightful description of the results and main ideas, their potential impact and importance to quantum machine learning. We encourage the submission of original work including work in progress and partial results. Extended abstracts on work submitted/published elsewhere are also welcome (a link to a separate published paper or preprint is required, in this case).
· Max. 2 pages, typeset in single-column form with reasonable margins and font size at least 11 points. References are excluded in page count.

Summary for full papers
· A non-technical, clear, and insightful description of the results and main ideas, their potential impact and importance to quantum machine learning. We encourage the submission of original work.
· Max. 5 pages, typeset in single-column form with reasonable margins and font size at least 11 points. References are excluded in page count.