The workshop aims to bridge the gap between traditional machine learning and quantum machine learning. Making this burgeoning technology of the future accessible to the computer scientists of today. Many practitioners of probabilistic machine learning techniques may be surprised to learn that much of their statistical expertise maps directly to the inherently stochastic domain of quantum physics. Researchers of generative models will find the sampling techniques made possible by quantum annealers of particular interest. Combinatorial optimization is another standard application of quantum computing, which is relevant to many ensemble methods. The most recent advances show viable neural networks on current and near-future quantum computers.

We invite contributed talks on completed work that focus on learning algorithms that are implementable on contemporary quantum hardware. Specific topics include, but are not limited to:
- Combinatorial optimization by quantum technologies for machine learning.
- Quantum-enhanced sampling and probabilistic machine learning.
- Alternative quantum technologies for machine learning, such as continuous-variable systems.
- Benchmarks of various quantum computers in machine learning problems.

Submit a one-page (max. 400 words) extended abstract to qmlkdd2018 at gmail.com.