In this video
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
Go deeper with a course
Optimize Your Stock Portfolio on a Real Quantum Computer
Dr. Muhammad Faryad
Tier-2 IBM Qiskit Advocate, IBM-certified Qiskit developer