Recent strides in quantum computing have raised the prospects that near term quantum devices can expediently solve computationally intractable problems in simulation, optimization and machine learning. The opportunities that quantum computing raises for machine learning is hard to understate. It opens the possibility of dramatic speedups for machine learning tasks, richer models for data sets and more natural settings for learning and inference than classical computing affords.
The goal of this workshop is, through a series of invited and contributed talks, survey the major results in this new area and facilitate increased dialog between researchers within this field and the greater machine learning community. Our hope is that such discussion will not only help researchers to fully leverage the promise of quantum machine learning but also address deep fundamental issues such as the question of what learning means in a quantum environment or whether quantum phenomena like entanglement may play a role in modeling complex data sets.
Session 1
9:00 – 10:00 Seth Lloyd (Intro to quantum machine learning)
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)
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?)
3:10 - 3:35 Steven Adachi (Application of quantum annealing to Training of Deep Neural Networks)
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)
5:10 - 5:50 Itay Hen (Fidelity-optimized quantum state estimation)
5:50 – 6:30 Harmut Neven (Emerging Quantum Processors and why the Machine Learning Community should care)
http://research.microsoft.com/en-us/events/qml/default.aspx
