QTML 2019: Quantum Techniques in Machine Learning 2019 Academic Cultural Complex, Korea Advanced Institute of Science and Technology (KAIST) Daejeon, South Korea, October 20-24, 2019 |
Conference website | https://www.quantummachinelearning.org/qtml2019.html |
Submission link | https://easychair.org/conferences/?conf=qtml2019 |
Poster deadline | August 1, 2019 |
Submission deadline | August 1, 2019 |
Decision notification | August 20, 2019 |
Early registration deadline | September 30, 2019 |
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 papers (20 min), and posters, on outstanding recent research 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 into quantum systems
- Quantum learning theory
- Fuzzy logic for quantum machine learning
- Using 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 extended abstracts should be in the PDF format.
Deadlines
- Extended abstract for short and full papers: August 1, 2019 at 23:59 (AoE)
- Extended abstract for poster: August 1, 2019 at 23:59 (AoE)
- Notification of acceptance: August 20, 2019 (AoE)
Extended abstract 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 one-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.
Post-conference publication
After the conference, all authors will be invited to prepare a full technical paper extending the work presented at the conference, taking advantage of the discussion during and after their presentation. These contributions will be submitted to Quantum Machine Intelligence (QUMI) for publication in a topical collection dedicated to QTML.
Contact
All questions about submissions should be emailed to June-Koo Kevin Rhee (rhee.jk-at-kaist.edu).