Build real QML models in Qiskit 2.4 for real datasets
If you want to build ML pipelines for real datasets, like Leukemia, breast cancer, iris, and financial data, to run on quantum computers in Qiskit 2.4, join this 10-session live course.
Topics include:
✅ Role of entanglement and measurements in QML
✅ Data encoding and feature maps
✅ Quantum kernels and k-means clustering
✅ Support vector machines using quantum kernels
✅ Quantum neural networks for classification
✅ QAOA and QUBO for Max-cut and financial applications
✅ Inclusion of quantum noise in the QML pipeline to mimic quantum hardware
✅ Training strategies and barren plateaus
Each lecture will have a theoretical foundation followed by a code walk-through of implementations.
If you are a student, researcher, data scientist, or industry professional looking to enter the field of Quantum AI, this course provides a structured pathway from fundamentals to advanced applications.
What you’ll learn
You will implement Quantum SVMs, Quantum neural networks, QAOA, Quantum k-means, and hybrid quantum-classical models in Qiskit 2.4.
Design quantum kernels, variational quantum classifiers, and optimization workflows.
Build quantum k-means clustering, quantum support vector machines, and quantum neural network classifiers in Qiskit 2.4.
Transform Max-cut and financial portfolio optimization QUBO-type problems into the QAOA framework
Learn basis, angle, Hamiltonian, and amplitude encoding schemes to select the appropriate strategy for a given problem.
Analyze circuit depth and complexity tradeoffs and understand how feature maps shape models' inductive bias.
Implement techniques for high-dimensional data by using re-uploading layers with Z, ZZ, Pauli, Efficient SU2, and other techniques.
Write, debug, and optimize quantum codes using Qiskit, Qiskit_IBM_Runtime, and Qiskit_Aer for IBM quantum hardware and simulators.
Build hybrid classical–quantum pipelines by integrating quantum models with PyTorch, Tensorflow, and Pandas.
Include quantum noise processes in the ML pipeline to mimic and take advantage of quantum noise in prediction
Learn directly from Muhammad
Dr. Muhammad Faryad
Quantum Machine Learning Scientist
Who this course is for
AI/ML Engineers & Researchers — Who want to implement, benchmark, and evaluate quantum ML models with theoretical depth and practical codes.
Computational Scientists — Who want to expand their computational tools to include practical quantum computing routines for data analysis.
Tech Leads — Who want to develop insights for quantum ML use-cases, hardware limits, and quantum-ready workforce.
Prerequisites
Classical Machine Learning Concepts
This course assumes knowledge and fluency with basic classical ML concepts, including supervised ML, SVMs, kernels, and neural networks.
Working Knowledge of Numpy, Sklearn, Pandas
The course will focus on using Qiskit for quantum ML programming, assuming students are already experienced in implementing ML problems.
This course is NOT for beginner students
This course is meant for experienced ML/AI engineers/leaders who are already well-versed in classical AI tools and want to learn quantum ML.
What's included
Live sessions
Learn directly from Dr. Muhammad Faryad in a real-time, interactive format.
Video Recordings
All live sessions will be recorded and made available to students right after each session.
End-to-end code files
All code files used during the live sessions will be delivered to students before showing end-to-end model building for real datasets.
Lecture slides
All lecture slides used during live sessions will be provided to students before each session.
Dedicated Q/A sessions
Live questions/answers sessions and discussion on QML and converting your ML projects to QML projects
Solved Assignments
Theory/coding problems with solutions
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Course syllabus
10 live sessions • 10 lessons
Week 1
Jun
16
1. Quantum states & gates, Bra-Ket language, Hilbert spaces, and Intro to Qiskit 2.4
Jun
18
2. Superposition, Interference, & entanlgement generation, Sampling measurements on Simulators & Hardware
Jupter Notebooks
Week 2
Jun
23
3. Measurements, observables, expectation values, and Estimator primitive implementation on Simulators and Hardware
Jun
24
4. Transition from ML to Quantum ML models
Jun
25
5. Data encoding and Quantum feature maps
Jupyter Notebooks
Free resources
Schedule
Live sessions
3-4 hrs / week
See syllabus for live sessions.
Tue, Jun 16
3:00 PM—4:15 PM (UTC)
Thu, Jun 18
3:00 PM—4:15 PM (UTC)
Tue, Jun 23
3:00 PM—4:15 PM (UTC)
Projects Proposals
1-2 hrs / week
The course will include proposals for several possible projects to pursue for the researchers.
Who has taken this course so far?
Founder, Quantum Computing Company, USA
Quantum Research Assistant, South Korea
Freelance ML developer, France
Physics Faculty Members, Pakistan, Morocco
Cyber Security Lecturer, UK
Graduate Students/Researchers, Pakistan, Turkey
Post-Doc Fellow, USA
Frequently asked questions
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