Please note that this website refers to a past event - for reference use only
May 8-10, 2019, at the Max Planck Institute for the Science of Light in Erlangen, Germany
This international workshop covered emerging applications of machine learning to quantum devices, including advanced methods like reinforcement learning.
The workshop was preceded by a two-day school for students trying to enter the field, going from a recap of the basics of neural networks to advanced techniques, May 6-7, 2019.
The 2-day mini-school (Monday/Tuesday) before the workshop, after a brief reminder about the basics, covered somewhat more advanced concepts like recurrent (memory) networks, reinforcement learning, Boltzmann machines, and a brief overview of applications of neural networks in various scientific fields. For more information, have a look at the slides (PDF) for the Tutorials in the School associated with the Workshop Machine Learning for Quantum Technology.
Scientific Organization: Florian Marquardt
Local Organization: Gesine Murphy
Invited Speakers (please click on talk titles to access talk slides):
- Hans Briegel, Innsbruck
- Evert van Nieuwenburg, Caltech: Integrating neural networks and quantum simulators
- Roger Melko, Waterloo: Generative models for (many-body) wavefunction reconstruction
- Barry Sanders, Calgary: Machine learning for quantum control
- Jens Eisert, Berlin: Quantum Machine Learning
- Paul Baireuther, Bosch Center for Artificial Intelligence (BCAI)
- Fabio Sciarrino, Rome
- Renato Renner, ETH Zürich: Discovering physical concepts with neural networks
- Marin Bukov, UC Berkeley: Reinforcement learning in different phases of quantum control
- Mauro Paternostro, Belfast: Supervise learning of time-independent Hamiltonians for gate design
- Natalia Ares, Oxford: Quantum device measurement and tuning using machine learning
- Emmanuel Flurin, CEA Saclay: Can a machine infer quantum dynamics?
- Enrico Prati, Consiglio Nazionale delle Ricerche
- Raffaele Santagati, Bristol
- Michael Hartmann, Edinburgh: Neural-Network Approach to Dissipative Quantum Many-Body Dynamics
There were also poster presentations, and short contributed talks. Talk slides can be accessed by clicking on the talk titles.
- Alexander Alodjants, ITMO University: Speedup problem for quantum walks and quantum annealing algorithms implementation
- Thomas Fösel, MPL: Reinforcement learning for quantum memory
- Thomas Gabor, LMU: QAR-Lab Site Report and the PlanQK Initiative
- Mats Granath, U of Gothenburg: Quantum error correction for the toric code using deep reinforcement learning
- Eliska Greplova, ETHZ: Hamiltonian learning for quantum error correction
- Niels Lörch, U of Basel: Unsupervised Learning of Phase Transitions
- Xiaotong Ni, TU Delft: Designing neural decoders for large toric codes
We would like to inform you that the talks were recorded, in accordance with data protection regulations (DSGVO).