Quantum Machine Learning for ML Engineers
Dr. Muhammad Faryad
Quantum Machine Learning Scientist
Future-proof your ML career—upgrade to quantum machine learning now.
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:
⚙️ Exponentially large Hilbert spaces for richer representations
🧠 Quantum feature maps for embedding data in new ways
🔁 Variational quantum models with compact expressivity
📈 Native kernel estimation via quantum circuits
But the real challenge is not theory—it is applicability.
❓ Where do quantum models fit in an ML pipeline?
❓ When does a quantum kernel outperform a classical one?
❓ How do you move from equations to working code on real hardware?
This course is designed to answer exactly these questions.
🎯 Focus on what is usable today—not distant promises
💻 Hands-on implementation using Qiskit
🔬 Build and test quantum feature maps, QSVMs, and variational classifiers
🔗 Learn how to integrate quantum models into existing ML workflows
By the end, you will not just understand quantum ML—you will know when, why, and how to apply it in practice.
What you’ll learn
You will implement Quantum SVMs, Quantum neural networks, Quantum feature maps, and hybrid quantum-classical models in Qiskit 2.3.
Implement quantum kernels, variational quantum classifiers, and optimization workflows.
Implement quantum SVMs, Quantum NNs, and QAOA in Qiskit.
Integrate classical optimizers in the hybrid training of quantum models, including quantum-specific data pre-processing.
Compare basis, angle, Hamiltonian, and amplitude encoding to select the appropriate strategy for a given problem.
Analyze qubit-depth and circuit complexity tradeoffs and understand how feature maps shape model inductive bias.
Implement techniques for high-dimensional data by using re-uploading layers.
Apply structured ansatz and initialization strategies to improve trainability by reducing barren plateaus.
Optimize circuits to reduce errors and learn to use various error mitigation strategies.
Write, debug, and optimize quantum circuits using Qiskit, Qiskit_IBM_Runtime, and Qiskit_Aer.
Build hybrid classical–quantum pipelines by integrating quantum models with PyTorch, Tensorflow, and Pandas
Manage backends, jobs, and transpilation workflows with quantum hardware access through IBM Cloud Platform.
Analyze the impact of noise on quantum ML algorithms
Understand various noise processes in quantum computers and be able to compare various available quantum hardware
Learn directly from Muhammad
Dr. Muhammad Faryad
Quantum Machine Learning Scientist
Who this course is for
Senior ML Engineers — Extend production ML expertise into quantum kernels, QNNs, and hybrid training loops to build next-gen models.
AI / ML Researchers — Reproduce, benchmark, and rigorously evaluate quantum ML methods with solid theory and implementation depth.
Tech Lead / Innovation Strategists — Develop technical clarity to assess QML feasibility, NISQ limits, and credible quantum-ready use cases.
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 ML students
This course is meant for experienced ML/AI engineers 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.
Jupyter Notebooks
All Jupyter notebooks used during the live sessions will be delivered to students before each session so they can practise alongside the instructor.
Lecture Material
All material (notes/slides) used during live sessions will be provided to students before each live session.
Maven Guarantee
Your purchase is backed by the Maven Guarantee.
Course syllabus
Week 1
May
20
1. Why Do ML Engineers Need Quantum Computing?
May
22
2. How Does Quantum Computing Give Computational Advantage?
Week 2
May
27
3. Why Measurements are Critical in Quantum Algorithms?
May
28
Optional: Q & A
May
29
4. How Do We Encode Classical Data into Quantum States?
Free resources
Schedule
Live sessions
3 hrs / week
This course has two required live sessions each week. The live sessions will include lectures and coding.
Wed, May 20
1:00 AM—2:15 AM (UTC)
Fri, May 22
1:00 AM—2:15 AM (UTC)
Wed, May 27
1:00 AM—2:15 AM (UTC)
Assignments and Projects
3-4 hrs / week
Assignments and projects will be distributed throughout the course, culminating in a final project report.
Frequently asked questions
$595
USD