Build your first quantum support vector machine

Hosted by Dr. Muhammad Faryad

308 students

In this video

What you'll learn

Quantum support vector machine model

Understand the key similarities and differences between classical and quantum SVMs.

Why quantum SVM can be better than classical SVM

Learn how quantum SVMs access exponentially larger kernel spaces without added computational cost.

Implement quantum SVM in Qiskit 2.3 on real dataset

Code a quantum SVM in Qiskit 2.3 and apply it to binary classification of a leukemia dataset.

Why this topic matters

Support Vector Machines underpin kernel methods in classical ML and provide a clear gateway to quantum advantage. Their reliance on inner products maps naturally to quantum state overlaps. Implementing Quantum SVMs in Qiskit enables direct comparison with classical models, clarifying where quantum feature maps and kernel evaluations may offer meaningful gains.

You'll learn from

Dr. Muhammad Faryad

IBM-Certified Quantum Developer, QML Scientist, Qiskit Advocate

Muhammad Faryad is a computational physicist and Associate Professor at LUMS. He earned his PhD in Engineering Science and Mechanics from The Pennsylvania State University in 2012, where he received the Best Dissertation Award. In 2019, he was honored with the Galleino Denardo Award from the Abdus Salam International Centre for Theoretical Physics (ICTP). He is an IBM-certified Qiskit 2.x developer, a Tier 2 IBM Qiskit Advocate, an IBM QAMP Mentor, and a QWorld Instructor. His research focuses on quantum machine learning, quantum algorithm design, and the analysis of noise resilience in near-term quantum systems.

Go deeper with a course

Quantum Machine Learning for ML Engineers
Dr. Muhammad Faryad
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