Quantum Machine Learning - Maria Schuld - MLSS 2020, Tübingen

Table of Contents (powered by https://videoken.com)
0:00:00 Speaker Introduction
0:01:13 Quantum Machine Learning
0:03:27 Quantum computing is an emerging technology.
0:08:52 How can quantum computers innovate ML?
0:11:35 Agenda
0:12:51 Quantum theory predicts expectations of measurements
0:12:57 Quantum Computing
0:23:12 Quantum computers perform linear algebra.
0:48:38 Data
0:49:37 Quantum computers cannot learn from "exponentially 1"
0:56:53 Algorithms
0:58:19 Quantum computers can invert exponentially large matrix
1:03:46 Quantum computers can train Boltzmann machines.*
1:07:20 Models
1:11:25 We can train quantum computations.
1:13:37 We can do gradient descent on variational circuits.
1:18:21 Quantum circuits are unitary neural nets in feature space
1:20:52 Quantum circuits are kernel methods.
1:23:47 Data encoding defines a "quantum kernel".
1:25:19 We can engineer/train our features.
1:27:11 Open Problems: Other questions than "are quantum computers better?":
1:28:39 Q&A

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