In recent years, the intersection of quantum computing (QC) and ML has emerged as a frontier of immense potential, promising revolutionary advancements across various fields. As we stand on the cusp of this transformative era, it becomes increasingly crucial to not only innovate new models and training strategies fit for near-term quantum computers, but also to seamlessly transfer the robust theoretical foundations of classical ML into the quantum domain.
We proudly announce “Quantum Machine Learning”, a half-day tutorial co-located with ECML PKDD 2024 in Vilnius, Lithuania, where we want to explore all the current motions in QML, placing a particular focus on the delicate balance between innovation and theoretical rigor. Through insightful discussions, interactive sessions, and hands-on tutorials, participants will delve into the intricacies of cutting-edge technology, gaining a comprehensive understanding of their strengths, limitations, and theoretical implications. By this we hope to ensure that future developments not only harness the transformative power of QC but also uphold the rigorous theoretical foundations that ML is built upon.
Call for Papers
We welcome submissions on topics matching or adjacent to any on the following non-exhaustive list:
- Quantum Algorithms for ML
- Quantum-Inspired Algorithms for ML
- ML Algorithms for Quantum Computing (QC)
- Theoretical Insights about QC
- Adapting ML Methods for QC
- Adiabatic QC (AQC) and Quantum Annealing for ML Problems
- QUBO or Ising Model Formulations of Optimization Problems
- Properties of QUBO or Ising Model Formulations
Submission instructions
We accept submissions of the following kinds:
- Previously unpublished full papers
up to 12 pages in LNCS format, excluding references - Poster presentations of recently published works
A0 format
Templates for the LNCS format can be found here. Please refer to this page and carefully follow the instructions regarding paper formatting (except page limit, which is 12 for this workshop) and double-blind policy, as well as authorship and conference attendance. These instructions apply for submissions to this workshop, as well. Submissions must be uploaded via CMT (choose track QML: Workshop on Quantum Machine Learning). Submissions will be evaluated by three reviewers on the basis of novelty, technical quality, potential impact, and clarity.
Key Dates
| Submission deadline | June 28, 2024 (Fri) extended! |
| Acceptance notification | July 22, 2024 (Fri) |
| Camera-ready deadline | August 23, 2024 (Fri) |
| Tutorial | September 13, 2024 (Fri) |
(All deadlines are 23:59 AoE)
Time Table
| 9:00 – 9:45 | Welcome & Introduction to Quantum Machine Learning |
| 9:45 – 10:30 | Advances in Quantum Machine Learning (I) Quantum Support Vector Machines |
| 10:30 – 10:40 | Coffee Break |
| 10:40 – 11:25 | Advances in Quantum Machine Learning (II) Probabilistic Quantum Models |
| 11:25 – 12:10 | Adiabatic Quantum Optimization in Practice |
| 12:10 – | Open Discussion & Lunch |
Workshop Organizers
- Sascha Mücke, TU Dortmund University, Dortmund, Germany
- Nico Piatkowski, Fraunhofer IAIS, Sankt-Augustin, Germany
- Christian Bauckhage, University of Bonn, Germany