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)
Intro to Universal Deep Quantum Learning (Talk)
Machine Learning as Rotations (Quantum Deep Learning) (Talk)
Quantum models for non-physical data at the example of item recommendation) (Talk)
Quantum ML via Matrix Inversion (Talk)
Totally Corrective Boosting with Cardinality Penalization (Talk)
Quantum-Inspired Graph Matching (Talk)
Application of quantum annealing to Training of Deep Neural Networks (talk)
Case Study towards deep learning (Talk)
Quantum Boltzmann Machine (Talk)
Fidelity-optimized quantum state estimation (Talk)
Emerging Quantum Processors and why the Machine Learning Community should care (Talk)
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