machine learning, quantum computing, programming, data analysis


Key Points:

  • Quantum computing

  • Machine learning

  • Deep learning


Detailed Description:

FIRE Quantum Machine Learning (QML) is the first course at the University of Maryland, College Park to introduce undergraduate students to a burgeoning new field at the intersection of the rich, established space of machine learning and artificial intelligence (ML/AI) and the newly evolving field of quantum computing (QC) and quantum machine learning.


Students will have the opportunity to pursue hands-on research and design of projects leveraging state-of-the-art methods unique to this bleeding-edge field, bolstering their knowledge and potentially paving the way for exciting future career outcomes.


Key Points:

  • Machine learning provides a means to solve complex tasks

  • Quantum computing circuitry enables very fast and highly parallelizable computation

  • Quantum machine learning has the potential to combine core strengths of both


Detailed Description:

Machine learning, and in particular deep learning via gradient descent, has been a key method for solving complex problems requiring machine inference, classification, and regression for several years now. 


Quantum computing predicated on exploitation of probabilistic hardware states has also been theorized for some time, but has only recently reached technological maturity. 


This course provides an opportunity to design, create, run and deploy quantum circuits and algorithms to solve complex tasks. This is a new and rare skill set that will certainly be in-demand as Quantum Computing and Quantum Machine Learning matures.


Key Points:

  • Conventional machine learning in python with keras, tensorflow, pandas, numpy

  • Quantum machine learning in python with IBM Qiskit™


Detailed description:

Students will learn to process complex data and train neural networks to perform classification, regression, clustering, and other complex cognitive tasks using standard data handling libraries such as pandas and numpy as well as machine learning frameworks like keras and tensorflow.


Students will be introduced to the theory behind Quantum Computing bits ("qubit") and the essential theory of quantum circuit design, and learn to use IBM Qiskit™ to deploy and run ML algorithms on complex multi-qubit hardware over IBM servers.


Ultimately, students will apply their knowledge in machine learning and quantum computing to design and implement a novel quantum machine learning project based on their own ideas that will use bleeding-edge, state-of-the-art QML techniques to solve complex cognitive tasks.

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