Course

Applied Quantum Machine Learning Using Qiskit

4.8 (25)

Muhammad Faryad

Associate Professor of Physics

This course is popular

3 people enrolled last week.

If you know ML, you already know most of QML.

If you understand training loops, optimization, kernels, and neural networks, you already know most of what quantum machine learning requires. The concepts map directly. The kernel trick becomes quantum kernels. Training loops become variational circuits. Classical optimization becomes quantum optimization. Neural networks become quantum neural networks. What's missing is the quantum layer, and that is exactly what this course teaches.

You will build every model in Qiskit 2.x and compare it with a classical baseline. You will also learn what QML cannot do yet.

What is included

  • 8 live lessons of 90 minutes each, all recorded

  • 12+ Jupyter notebooks with end-to-end QML pipelines on real datasets like genomic, credit-card fraud, NY stock exchange, etc.

  • PDF slides with full derivations, written as a standalone reference

  • A certificate of completion

🐦 🏷️Early-bird discount for first 10 students: 30% off using code EQML

What you’ll learn

Go from ML to QML in 3 weeks. Build quantum models on real data using QAOA, quantum neural networks, and quantum kernels.

  • States, gates, and measurement taught as the linear algebra you already use

  • Superposition, interference, and entanglement as computational resources

  • Qiskit 2.x primitives, SamplerV2 and EstimatorV2, from day one

  • Basis, amplitude, and angle encoding, and when to use each

  • Design quantum feature maps as feature engineering

  • See how encoding choices shape what a model can learn

  • Extend the kernel trick to quantum feature spaces

  • Train QSVMs on leukemia and breast cancer datasets

  • Cluster data with quantum k-means

  • Variational circuits as the quantum version of a training loop

  • Choose ansätze, cost functions, and optimizers that work

  • Diagnose barren plateaus and other trainability failures

  • Cast stock portfolio selection as a QUBO

  • Run QAOA on the simulator and IBM quantum hardware

  • Compare the best stock option with the verified classical optimal

Learn directly from Muhammad

Muhammad Faryad

Muhammad Faryad

Contact

Associate Professor of Physics, LUMS · Tier-2 IBM Qiskit Advocate

Abdus Salam International Centre for Theoretical Physics
Penn State College of Engineering
Ayass BioScience
Qiskit
LUMS
See all products from Dr. Muhammad Faryad

Who this course is for

  • AI/ML engineers — You want to add quantum ML to your skill set and be the person on your team who can prototype and evaluate it.

  • Freelance data scientists and developers — You want a rare niche that sets your profile apart, with end-to-end notebooks on real datasets.

  • Graduate students and researchers — You want to start research in quantum ML with solid theory and working code.

Prerequisites

  • Core machine learning

    You are fluent in supervised and unsupervised learning, SVMs, and neural networks.

  • Python with NumPy, scikit-learn, and pandas

    You have written ML code in Python before. We build on these tools from day one.

  • Basic linear algebra

    You are comfortable with vectors and matrix multiplication. Exposure to eigenvalues and complex numbers helps you go deeper.

What's included

Muhammad Faryad

Live sessions

Learn directly from Muhammad Faryad in a real-time, interactive format.

Recordings of every session

Every live session is recorded and posted right after it ends.

Fully tested Qiskit notebooks

Each notebook is shared before its session, so you can run along as models are built on real data.

Slides with full derivations

Shared before each session and written to work as a standalone reference.

Live Q&A in every session

Bring your questions, including how to turn your own ML projects into QML projects.

Practice notebook with solutions

A practice assignment with full solutions to check your Qiskit fundamentals.

Guidance to convert your ML projects to QML

Consult the instructor freely to convert your ML projects to QML projects.

Certificate of completion

Issued to every participant who completes the course.

Maven Guarantee

Your purchase is backed by the Maven Guarantee.

Course syllabus

9 live sessions • 24 lessons

Live sessions

3-4 hrs / week

See the syllabus for the detailed schedule.

Work through the notebooks

2-3 hrs / week

Run and change the notebooks after each session. This is where most of the learning happens.

Review the slides

1-2 hrs / week

Skim the slides before each session and review them after. They hold the full math.

Week 1

Dec 1—Dec 6

    Dec

    1

    Lesson 1 — Qubits, Gates, Qiskit Setup, and Your First Quantum Circuit

    Tue 12/14:00 PM—5:30 PM (UTC)

    Dec

    2

    Lesson 2 — Interference, Entanglement, and Qiskit Primitives

    Wed 12/24:00 PM—5:30 PM (UTC)

    Dec

    3

    Lesson 3 — Quantum Machine Learning Models

    Thu 12/34:00 PM—5:30 PM (UTC)

    Slides

    3 items

    Jupyter Notebooks

    5 items

    Practice Problems with Solutions

    2 items

    Accessing IBM Hardware

    1 item

Week 2

Dec 7—Dec 13

    Dec

    8

    Lesson 4 — Data Encoding and Quantum Feature Maps

    Tue 12/84:00 PM—5:30 PM (UTC)

    Dec

    9

    Lesson 5 — Quantum Neural Networks on Real Datasets

    Wed 12/94:00 PM—5:30 PM (UTC)

    Dec

    10

    Optional: Q & A: Bring your questions

    Thu 12/104:00 PM—5:00 PM (UTC)
    Optional

    Slides

    2 items

    Jupyter Notebooks

    2 items

Free resources

Alumni

Alumni in 14 countries, from Chewy and Teradata to the University of Chicago and Queen's University.

Industry Alumni

  • Staff Machine Learning Engineer, Chewy, Florida, USA

  • Senior Data Scientist, Teradata, Pakistan

  • Solution Advisor, Omnicell, Texas, USA

  • Data Scientist, Acorn Bioenergy, London, UK

  • Software Engineer, HSS, New York, USA

  • QC Coordinator, Amerisci, New York, USA

  • Founder, quantum computing company, USA

  • Research Assistant, quantum computing company, South Korea

  • Actuary, Sompo Insurance, Mexico

  • AVP Retail & Projects, Valiram Group, Malaysia

  • Senior AI/ML Engineer, PITB, Pakistan

  • Data & AI Engineer, Measurebit, Maharashtra, India

  • Architect, L&T, Telangana, India

  • Software Engineer, Innovate for Consulting and Technology, Cairo, Egypt

  • Software Engineer, Kaung Sein Tech, Myanmar

  • Freelance software developers, UK and France

Academic Alumni

  • Informatics Researcher, University of Chicago, USA

  • Postdoctoral Researcher, Saint Louis University, USA

  • Research Assistant, Binghamton University, New York, USA

  • Graduate Student, UC Riverside, California, USA

  • PhD Student, Queen's University, Ontario, Canada

  • Lecturer, Aston University, UK

  • Lecturer, Northern Border University, Saudi Arabia

  • Quantum Physics Faculty, Mohammed V University, Morocco

  • Faculty, Ibn Zohr University, Morocco

  • Faculty and students, LUMS and QAU, Pakistan

  • Students, IISER Mohali and IIT Kottayam, India

  • Student, Çanakkale Onsekiz Mart University, Türkiye

  • Student, Universidad Anáhuac México Norte, Mexico

Frequently asked questions

Maven for Teams

Reimbursement

Get your company to pay

Everything L&D needs: email template, receipts, and certificate of completion.

Get reimbursed

Team discount

Learn with your teammates

Save 20%+ when 2 or more teammates enroll in the same cohort.

Save 20%+ with a team

Private cohort

Run a cohort for your org

A dedicated cohort with a custom schedule and curriculum, tailored to your team.

Book a private cohort

$499

USD

·
Dec 1—Dec 17
Enroll