Session 1
| 9:00 – 10:00 | Seth Lloyd (Intro to quantum computing and quantum machine learning) Slides | Slides |
Session 2
| 10:30 – 11:10 | Ashish Kapoor (Quantum Deep Learning) |
| 11:10 – 11:50 | Cyril Stark (Quantum models for non-physical data at the example of item recommendation) | Slides |
| 11:50 – 12:30 | Patrick Rebentrost (TBA) |
| 12:30 – 12:45 | Vasil Denchev (Totally Corrective Boosting with Cardinality Penalization) |
| 12:45 – 1:00 | Luca Rossi (Quantum-Inspired Graph Matching) |
Session 3
| 2:30 – 3:10 | Nathan Wiebe (Can small quantum systems learn?) | Slides |
| 3:10 – 3:35 | Steven Adachi (Application of quantum annealing to Training of Deep Neural Networks) | Slides |
| 3:35 – 4:00 | Alejandro Perdomo (Estimation of effective temperatures in a quantum annealer and its impact in sampling applications: A case study towards deep learning). |
Session 4
| 4:30 – 5:10 | Mohammad Amin (Quantum Boltzmann Machine) | Slides |
| 5:10 – 5:50 | Itay Hen (Fidelity-optimized quantum state estimation) | Slides |
| 5:50 – 6:30 | Harmut Neven (Emerging Quantum Processors and why the Machine Learning Community should care) |