Quantum Machine Learning

Quantum computers are becoming available, which begs the question: what are we going to use them for? Machine learning is a good candidate. In this course we will introduce several quantum machine learning algorithms and implement them in Python.

( 0 Reviews )

Created by: Peter Wittek

Quality Score

Content Quality

Video Quality

Qualified Instructor

Course Pace

Course Depth & Coverage

Overall Score : 0 / 100

Course Description

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 Aln 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.

Advanced Level

Time: 9 Weeks - 8-10 hours per week

Instructor Details

Peter Wittek

0.0 Rating
0 Reviews

Peter Wittek is an Assistant Professor in the University of Toronto and an affiliate in the Vector Institute for Artificial Intelligence the Perimeter Institute for Theoretical Physics. He obtained his PhD from the National University of Singapore. His research explores the synergies between artificial intelligence, machine learning, quantum information theory, and quantum computing. As the Academic Director of the Quantum Program in the Creative Destruction Lab, he oversees two dozen quantum software startups a year that exploit contemporary quantum technologies in a commercial setting.