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:
Schedule
13:00-13:30 Quantum machine learning introduction (Christopher Watkins, Commonwealth Scientific and Industrial Research Organisation (CSIRO))
13:30-14:10 Keynote: Ashley Montanaro, University of Bristol
14:10-14:30 A quantum alternating operator ansatz with hard and soft constraints for lattice protein folding (Tomas Babej, ProteinQure)
14:30-15:00 Break
15:00-15:20 Quantum computing for kernel methods (Maria Schuld, University of KwaZulu-Natal, National Institute for Theoretical Physics, Xanadu)
15:20-15:40 Driven Tabu Search: A Quantum Inherent Optimisation (Carla Silva, University of Porto)
15:40-16:00 Hyperspectral image segmentation using adiabatic quantum computation (Piotr Gawron, Polish Academy of Sciences)
16:00-16:20 Quantum generative adversarial learning (Seth Lloyd, Massachusetts Institute of Technology)
16:20-17:00 Panel discussion
Important dates
Workshop paper submissions: May 8, 2018
Workshop paper notifications: June 8, 2018
Organizers
Christopher Watkins, Commonwealth Scientific and Industrial Research Organisation (CSIRO)
Peter Wittek, University of Toronto, Creative Destruction Lab, Vector Institute for Artificial Intelligence, and Perimeter Institute for Theoretical Physics
Peter Mountney, Siemens Healthineers and University College London
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.
Schedule
13:00-13:30 Quantum machine learning introduction (Christopher Watkins, Commonwealth Scientific and Industrial Research Organisation (CSIRO))
13:30-14:10 Keynote: Ashley Montanaro, University of Bristol
14:10-14:30 A quantum alternating operator ansatz with hard and soft constraints for lattice protein folding (Tomas Babej, ProteinQure)
14:30-15:00 Break
15:00-15:20 Quantum computing for kernel methods (Maria Schuld, University of KwaZulu-Natal, National Institute for Theoretical Physics, Xanadu)
15:20-15:40 Driven Tabu Search: A Quantum Inherent Optimisation (Carla Silva, University of Porto)
15:40-16:00 Hyperspectral image segmentation using adiabatic quantum computation (Piotr Gawron, Polish Academy of Sciences)
16:00-16:20 Quantum generative adversarial learning (Seth Lloyd, Massachusetts Institute of Technology)
16:20-17:00 Panel discussion
Important dates
Workshop paper submissions: May 8, 2018
Workshop paper notifications: June 8, 2018
Organizers
Christopher Watkins, Commonwealth Scientific and Industrial Research Organisation (CSIRO)
Peter Wittek, University of Toronto, Creative Destruction Lab, Vector Institute for Artificial Intelligence, and Perimeter Institute for Theoretical Physics
Peter Mountney, Siemens Healthineers and University College London
