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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.
Sat 5:30 a.m. - 6:00 a.m.
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Can Small Quantum systems Learn?
(talk)
|
Nathan Wiebe 🔗 |
Sat 6:20 a.m. - 7:30 a.m.
|
Intro to Universal Deep Quantum Learning
(Talk)
|
Seth Lloyd 🔗 |
Sat 7:30 a.m. - 8:00 a.m.
|
Machine Learning as Rotations (Quantum Deep Learning)
(Talk)
|
Ashish Kapoor 🔗 |
Sat 8:10 a.m. - 8:40 a.m.
|
Quantum models for non-physical data at the example of item recommendation)
(Talk)
|
Cyril Stark 🔗 |
Sat 8:50 a.m. - 9:20 a.m.
|
Quantum ML via Matrix Inversion
(Talk)
|
Patrick Rebentrost 🔗 |
Sat 9:30 a.m. - 9:45 a.m.
|
Totally Corrective Boosting with Cardinality Penalization
(Talk)
|
Vasil Denchev 🔗 |
Sat 9:45 a.m. - 10:00 a.m.
|
Quantum-Inspired Graph Matching
(Talk)
|
Luca Rossi 🔗 |
Sat 12:10 p.m. - 12:40 p.m.
|
Application of quantum annealing to Training of Deep Neural Networks
(talk)
|
Steven Adachi 🔗 |
Sat 12:40 p.m. - 1:10 p.m.
|
Case Study towards deep learning
(Talk)
|
Alejandro Perdomo-Ortiz 🔗 |
Sat 1:30 p.m. - 2:10 p.m.
|
Quantum Boltzmann Machine
(Talk)
|
Mohammad Amin 🔗 |
Sat 2:10 p.m. - 3:00 p.m.
|
Fidelity-optimized quantum state estimation
(Talk)
|
Itay Hen 🔗 |
Sat 3:00 p.m. - 3:45 p.m.
|
Emerging Quantum Processors and why the Machine Learning Community should care
(Talk)
|
Hartmut Neven 🔗 |
Author Information
Nathan Wiebe (Microsoft Research)
Seth Lloyd (MIT)
More from the Same Authors
-
2022 Poster: projUNN: efficient method for training deep networks with unitary matrices »
Bobak Kiani · Randall Balestriero · Yann LeCun · Seth Lloyd -
2019 Poster: Random deep neural networks are biased towards simple functions »
Giacomo De Palma · Bobak Kiani · Seth Lloyd -
2016 Poster: Quantum Perceptron Models »
Ashish Kapoor · Nathan Wiebe · Krysta Svore -
2015 : Intro to Universal Deep Quantum Learning »
Seth Lloyd -
2015 : Can Small Quantum systems Learn? »
Nathan Wiebe