Cohort 1

Founder

Guillermo Gonzalez

Engineers and engineering leaders who build, deploy, and scale software systems.

Data scientists who apply statistical and ML techniques to extract insights from data.

Applying AI tools and techniques to professional workflows — assign alongside more specific AI topics.

Building, training, and deploying machine learning models — from classical ML to deep learning.

Go from ML to QML in 4 weeks. Build quantum models on real clinical data and finish with a capstone project you can show.

Associate Professor of Physics

Abdus Salam International Centre for Theoretical Physics

Penn State College of Engineering

Ayass BioScience

Qiskit

LUMS

Muhammad Faryad

Associate Professor of Physics, LUMS · Tier-2 IBM Qiskit Advocate

Applied Quantum Machine Learning Using Qiskit

Dr. Faryad

Quantum Machine Learning Scientist, IBM-certified Qiskit developer

<h3>Why I enrolled</h3>
<p class="p2-tall"><strong>I want to learn a bit more about quantum machine learning in order to expand my researech outside of QCNNs and possibly devise some new algorithms specifically for quantum computers.</strong></p>
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    <h3>A breakthrough moment</h3><p class="p2-tall">Well for sure the 'aha' moment for this course form me is the lesson presented on April 14. Although I have been working with the Qiskit QCNN, your basic build up of the straightforward example was highly beneficial.  Walking through the code helped tremendously.</p>

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<h3>What I’ll apply</h3>
<p class="p2-tall">I plan to run each of the algorithm programs that were discussed and shared by the Professor. I also want to go back and re-review the buildup to some of the problems as they were explained in great detail. </p>
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Certificate

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