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Quantum Technology

Quantum Machine Learning Implementation

A hands-on 3-day course on designing, training and evaluating hybrid quantum-classical machine learning models using Qiskit and PennyLane, with a working QML model developed by the end of the course.

Course Syllabus

Quantum Data Encoding & Circuits

Build parameterised circuits as trainable components.

Quantum Classifiers & Kernels

Variational classifiers and Quantum SVMs compared with classical approaches.

Hybrid Quantum-Classical Networks

Use quantum layers in PyTorch and train them end to end.

Real-World Trainability

Tackle barren plateaus, overfitting and hardware noise.

Hands-On Learning

  • 7 labs in Jupyter Notebooks
  • Real datasets and classical benchmarks
  • Day 3 capstone with live demo

    • Tools & Techniques

  • Qiskit (IBM)
  • PennyLane (Xanadu)
  • Simulators and cloud quantum hardware
  • Noise-aware training techniques
  • Capstone peer review

    • Prerequisites

  • Python
  • An ML framework
  • Basic quantum knowledge

    • Training Venue Details

    This course is conducted physically inside our state-of-the-art research laboratories. Attendees will gain direct hands-on access to advanced machinery, monitoring environments, and dedicated testing frameworks under professional supervision.

    Lab Location:MIMOS Berhad, Bukit Jalil

    Course Specifications

    Course FeeContact Academy for Fee Details (HRD Corp Claimable)
    Course Duration3 Days
    Conduct MethodPhysical (Classroom & Lab)
    Next Active BatchScheduled SoonRegistration closing 7 days prior to start date.
    Register Interest
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