Build Quantum Neural Networks in Qiskit

Hosted by Dr. Muhammad Faryad

Wed, May 6, 2026

1:00 AM UTC (1 hour)

Virtual (Zoom)

Free to join

Invite your network

Go deeper with a course

Quantum Machine Learning for ML Engineers
Dr. Muhammad Faryad
View syllabus

What you'll learn

Quantum neural network model

Understand the key similarities and differences between classical and quantum neural network.

Why quantum NNs can be better than classical NNs

Learn how quantum NNs access exponentially larger vector spaces.

Implement quantum NNs in Qiskit 2.3 on real dataset

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

Why this topic matters

Quantum ML is moving from theory to practice, and tools like Qiskit make it accessible today. Learning to code quantum neural networks helps you move beyond concepts into real experiments, positioning you early in a rapidly evolving field. QNNs represent and process information by leveraging superposition, entanglement, and high-dimensional Hilbert spaces in ways classical models simply cannot.

You'll learn from

Dr. Muhammad Faryad

IBM-Certified Quantum Developer, QML Scientist, Qiskit Advocate

Muhammad Faryad is an experienced Maven instructor 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.

Sign up to join this lesson

By continuing, you agree to Maven's Terms and Privacy Policy.