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Quantum Machine Learning

University of Toronto via edX

1 Review 124 students interested

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Overview

The pace of development in quantum computing mirrors the rapid advances made in machine learning and artificial intelligence. It is natural to ask whether quantum technologies could boost learning algorithms: this field of inquiry is called quantum-enhanced machine learning. The goal of this course is to show what benefits current and future quantum technologies can provide to machine learning, focusing on algorithms that are challenging with classical digital computers. We put a strong emphasis on implementing the protocols, using open source frameworks in Python. Prominent researchers in the field will give guest lectures to provide extra depth to each major topic. These guest lecturers include Alán Aspuru-Guzik, Seth Lloyd, Roger Melko, and Maria Schuld.

In particular, we will address the following objectives:

1) Understand the basics of quantum states as a generalization of classical probability distributions, their evolution in closed and open systems, and measurements as a form of sampling. Describe elementary classical and quantum many-body systems.

2) Contrast quantum computing paradigms and implementations. Recognize the limitations of current and near-future quantum technologies and the kind of the tasks where they outperform or are expected to outperform classical computers. Explain variational circuits.

3) Describe and implement classical-quantum hybrid learning algorithms. Encode classical information in quantum systems. Perform discrete optimization in ensembles and unsupervised machine learning with different quantum computing paradigms. Sample quantum states for probabilistic models. Experiment with unusual kernel functions on quantum computers

4) Demonstrate coherent quantum machine learning protocols and estimate their resources requirements. Summarize quantum Fourier transformation, quantum phase estimation and quantum matrix, and implement these algorithms. General linear algebra subroutines by quantum algorithms. Gaussian processes on a quantum computer.

Taught by

Peter Wittek

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Anonymous
5.0 an hour ago
Anonymous is taking this course right now.
Really an excellent course. The plethora of concepts required to appreciate the field are neatly put together. With the hands-on kind of approach, one can really get fantastic feel and appreciation of the necessary basics and techniques. Crossed the passing grade of this exciting course and planning to score more in the coming weeks.
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