Free Lesson

Unsupervised Quantum Machine Learning

Hosted by Dr. Muhammad Faryad

Thu, Sep 3, 2026

4:00 PM UTC (1 hour)

Virtual (Zoom)

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115 students

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Quantum Machine Learning using Qiskit 2.x
Dr. Muhammad Faryad
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What you'll learn

Why unsupervised quantum ML?

Learn how quantum computing can compute similarity between data points in exponentially big Hilbert spaces.

How data points are mapped to quantum states?

Learn to encode data points in quantum states, and replace kernel trick with using various quantum feature maps

How to implement quantum k-means clustering in Qiskit 2.x?

Implement in k-means pipeline in Qiskit 2.x on real dataset like iris.

Why this topic matters

✨ Clustering powers fraud detection, genomics & customer segmentation — quantum k-means reveals structure classical distance metrics miss. ⭐ Few ML engineers can bridge classical & quantum pipelines — a rare, high-demand skill as hiring shifts toward quantum-ready talent. 🚀 You'll leave able to implement quantum-enhanced clustering on real data in Qiskit — working code, not just theory.

You'll learn from

Dr. Muhammad Faryad

Tier-2 IBM Qiskit Advocate

Muhammad Faryad is an experienced Maven instructor and quantum machine learning scientist. He earned his PhD in Engineering Science and Mechanics from The Pennsylvania State University in 2012. He was honored with the Gallieno Denardo Award from the Abdus Salam International Centre for Theoretical Physics (ICTP) in 2019. He is a Tier 2 IBM Qiskit Advocate, an IBM-certified Qiskit 2.x developer, an IBM QAMP Mentor, and a QWorld Instructor.

Ex. Penn State, QWorld, ICTP

QWorld
Qiskit
LUMS
Penn State College of Engineering
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