Abdus Salam International Centre for Theoretical Physics

Penn State College of Engineering

Qiskit

LUMS

Muhammad Faryad

Quantum Machine Learning Scientist, Trainer and Consultant, IBM-certified Qiskit developer

Quantum School

Cohort 1

Hands-on workshop on data encodings, quantum neural networks, and quantum kernels using Qiskit.

Cohort 2

Serious about Quantum Machine Learning? This resource gives you structured access to my full QML workshop series on YouTube and complete implementation notebooks on GitHub. You’ll get practical Qiskit code for quantum SVMs and QNNs, built for students with quantum computing experience. The workshop is for those who want real implementation depth, clear intuition, and reproducible experiments, not hype.

Lectures and Jupyter Notebooks of QML Workshop

You will implement Quantum SVMs, Quantum neural networks, Quantum feature maps, and hybrid quantum-classical models in Qiskit 2.3.

Quantum Machine Learning Scientist

Ayass BioScience

Dr. Muhammad Faryad

Classical machine learning already pushes limits with kernel methods, optimization, and high-dimensional feature spaces. As models scale, so do the bottlenecks—training cost, feature engineering complexity, and diminishing returns.Quantum computing introduces a fundamentally different regime:<ul><li>⚙️ Exponentially large Hilbert spaces for richer representations</li><li>🧠 Quantum feature maps for embedding data in new ways</li><li>🔁 Variational quantum models with compact expressivity</li><li>📈 Native kernel estimation via quantum circuits</li></ul>But the real challenge is not theory—it is applicability.<ul><li>❓ Where do quantum models fit in an ML pipeline?</li><li>❓ When does a quantum kernel outperform a classical one?</li><li>❓ How do you move from equations to working code on real hardware?</li></ul>This course is designed to answer exactly these questions.<ul><li>🎯 Focus on what is usable today—not distant promises</li><li>💻 Hands-on implementation using Qiskit</li><li>🔬 Build and test quantum feature maps, QSVMs, and variational classifiers</li><li>🔗 Learn how to integrate quantum models into existing ML workflows</li></ul>By the end, you will not just understand quantum ML—you will know when, why, and how to apply it in practice.

Upskill your ML career: Integrate quantum computing with machine learning.

Build and train quantum models end-to-end

Master quantum data encoding strategies

Develop production-ready quantum programming skills

Understand constraints of current quantum hardware for practical programming

This course assumes knowledge and fluency with basic classical ML concepts, including supervised ML, SVMs, kernels, and neural networks.

Classical Machine Learning Concepts 

The course will focus on using Qiskit for quantum ML programming, assuming students are already experienced in implementing ML problems. 

Working Knowledge of Numpy, Sklearn, Pandas

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.

This course is NOT for beginner ML students

Senior ML Engineers — Extend production ML expertise into quantum kernels, quantum neural networks, and hybrid training loops

AI / ML Researchers — Reproduce, benchmark, and rigorously evaluate quantum ML models with solid theoretical depth and practical codes.

Tech Lead / Innovation Strategists — Develop insights for quantum machine learning, hardware limits, and credible quantum-ready use cases.

Auditing

Trainings

Quantum Algorithms and Quantum ML Scientist

Get your bussiness ready for the quantum era

Quantum ML in 4 Hours: Build Real QNNs & Kernels in Qiskit

Most ML engineers are hearing about quantum advantage—but have no practical way to evaluate or use it. This creates a real professional gap: you’re expected to understand quantum ML, yet most resources are either too theoretical or too fragmented to apply. This workshop solves that gap directly.In just 4 hours, you’ll move from zero intuition to actually building and testing quantum models—quantum kernels, QSVMs, and QNNs—using Qiskit. You won’t just “learn concepts”; you’ll implement circuits, encode data into feature maps, and see how quantum models behave on real problems. All you need to start is the basics of quantum computing. If you are familiar with Qiskit and quantum computing, this workshop will take you to Quantum ML applications.Why this matters now:<ul><li>Quantum computing is transitioning from hype to early adoption—teams need practitioners, not observers.</li><li>Hiring managers increasingly value “applied exposure” to quantum tools.</li><li>Understanding when quantum models help (and when they don’t) is a competitive edge.</li></ul>

The workshop assumes that participants have basic knowledge of quantum computing and now want to go deeper into quantum ML. 

What basic knowledge is required to benifit from this workshop?

No. But an understanding of basic gates and circuit language of quantum computing is required. We will not review quantum gates.

Is prior Qiskit programming experience required?

Yes, the workshop assumes that participants have an understanding of ML/AI concepts and want to transition to Quantum ML/AI.

Is ML experience required?

ML Engineers: Extend ML expertise into quantum kernels and quantum neural networks if you have basic quantum computing knowledge.

AI / ML Researchers: Reproduce, benchmark, and rigorously evaluate quantum ML models with solid theoretical depth.

Tech Lead / Founders: Develop insights for quantum machine learning, hardware limits, and credible quantum-ready use cases.

Build Quantum Neural Networks in Qiskit

IBM-Certified Quantum Developer, QML Scientist, Qiskit Advocate

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

Quantum neural network model

Learn how quantum NNs access exponentially larger vector spaces.

Why quantum NNs  can be better than classical NNs

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

Implement quantum NNs in Qiskit 2.3 on real dataset

Build your first quantum support vector machine

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

Quantum support vector machine model

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

Why quantum SVM can be better than classical SVM

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

Muhammad Faryad will help you integrate ML & quantum computing

Muhammad Faryad

About Dr. Faryad

Courses

Quantum Machine Learning for ML Engineers

Workshops

Quantum Machine Learning Workshop

Lightning Lessons

Build Quantum Neural Networks in Qiskit

Build your first quantum support vector machine

Services

Get your bussiness ready for the quantum era

Resources

Lectures and Jupyter Notebooks of QML Workshop