Quantum Machine Learning for ML Engineers
Dr. Muhammad Faryad
Quantum Machine Learning Scientist
Upskill your ML career: Integrate quantum computing with machine learning.
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.
Design quantum kernels, variational quantum classifiers, and optimization workflows.
Build quantum support vector machines and quantum neural networks in Qiskit.
Integrate classical optimizers in the hybrid training of quantum models, including quantum-specific data pre-processing and rescaling.
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.
Manage backends, jobs, and transpilation workflows with access to quantum hardware through IBM Cloud Platform.
Understand the classical simulation of quantum algorithms to validate quantum codes before hardware execution appropriately.
Understand various noise processes in quantum computers and be able to compare various available quantum hardware.
Leverage available error mitigation techniques while running circuits on quantum hardware at the transpilation and execution stages.
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, 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.
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/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.
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.
Separate live 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
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
2-3 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
2-3 hrs / week
Assignments and optional projects' discussion will be distributed throughout the course.
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
$595
USD