Introduction to Generative Modeling Using Quantum Machine Learning


Ever wondered how quantum computers work, and how they do machine learning? With quantum computing technologies nearing the ear of commercialization and quantum advantage, machine learning has been proposed as one of the most promising applications. One of the areas in which quantum computing is showing great potential is in generative models in unsupervised and semi-supervised learning.
In this training, you will develop a basic understanding of quantum computing and how it can be used in machine learning models, with special emphasis on generative models. We will focus on a particular architecture, the quantum circuit Born machine (QCBM), and use it to generate a simple dataset of bars and stripes.
No previous knowledge of quantum computing and the generative model is needed for this workshop.

Session Outline
Module 1: Generative Machine Learning
A brief overview of machine learning and generative machine learning, including the notions of generative adversarial networks and how they are used to generate realistic images.

Module 2: Quantum Computing
An introduction to what is quantum computing, including the notions of a qubit, Bloch sphere, quantum gates, quantum measurement, and entanglement.

Module 3: Quantum Generative Models
In this module, we learn how to build a quantum circuit and use it to build generative models. We’ll study the quantum circuit Born machine (QCBM) in more detail. Then we’ll code one in a Jupyter notebook using a quantum machine learning package. Finally, there will be a demo of Orquestra, a platform for writing and deploying code in quantum computers.

Background Knowledge
Background in machine learning and in programming and familiarity with Jupyter notebooks.
No knowledge of quantum computing or generative models required as we will be developing this knowledge as needed, but some foundational math knowledge such as matrix arithmetic, linear algebra, and probability is recommended.


Kaitlin Gili is a Quantum Applications Intern at Zapata Computing. She has previously worked at Los Alamos National Laboratory and the IBMQ hub within Keio University as a quantum algorithm intern, and at the University of Oxford as a visiting quantum hardware research student. Kaitlin is passionate about quantum computing outreach for young scientists and has previously delivered quantum computing workshops to Girls Who Code middle/high school programs. She received her Bachelors's in Physics from Stevens Institute of Technology and will be starting her PhD in Physics at the University of Oxford in January 2021.

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