Quantum Machine Learning


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. 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.

We will be accepting contributed talks as well. The deadline for submission is Oct 24. An abstract and link to the paper should be provided. We will provide feedback on submissions before the deadline for early registration for NIPS. All talks given will not be published in the proceedings and will be reviewed by the conference organizers. Please sent all contributions to nawiebe@microsoft.com.

The workshop is organized by Nathan Wiebe (Microsoft Research) and Seth Lloyd (MIT).

It will take place at Palais des Congrès de Montréal, Montréal, Canada as part of NIPS (Neural Information Processing Systems).

The workshop will consist of 4 invited talks as well as 4 contributed talks and a primer on quantum mechanics and quantum computing. Ample time will also be allocated to allow discussion between the attendees.


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)