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Sat Dec 12 05:30 AM -- 03:30 PM (PST) @ 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.

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