Program Highlights »
Sat Dec 12th 08:30 AM -- 06:30 PM @ 512 a
Quantum Machine Learning
Nathan Wiebe · Seth Lloyd

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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.

08:30 AM Can Small Quantum systems Learn? (talk)
Nathan Wiebe
09:20 AM Intro to Universal Deep Quantum Learning (Talk)
Seth Lloyd
10:30 AM Machine Learning as Rotations (Quantum Deep Learning) (Talk)
Ashish Kapoor
11:10 AM Quantum models for non-physical data at the example of item recommendation) (Talk)
Cyril Stark
11:50 AM Quantum ML via Matrix Inversion (Talk)
Patrick Rebentrost
12:30 PM Totally Corrective Boosting with Cardinality Penalization (Talk)
Vasil Denchev
12:45 PM Quantum-Inspired Graph Matching (Talk)
Luca Rossi
03:10 PM Application of quantum annealing to Training of Deep Neural Networks (talk)
Steve Adachi
03:40 PM Case Study towards deep learning (Talk)
Alejandro Perdomo-Ortiz
04:30 PM Quantum Boltzmann Machine (Talk)
Mohammad Amin
05:10 PM Fidelity-optimized quantum state estimation (Talk)
Itay Hen
06:00 PM Emerging Quantum Processors and why the Machine Learning Community should care (Talk)
Hartmut Neven